To program the knapsack problem recursively, you can define a recursive function that takes in the current item and the remaining capacity of the knapsack. Within the function, you can consider two cases: including the current item in the knapsack and excluding it. For the case of including the item, you would recursively call the function with the next item and the reduced capacity of the knapsack. For the case of excluding the item, you would recursively call the function with the next item and the original capacity of the knapsack. You would then return the maximum value obtained from these two cases. This approach allows you to consider all possible combinations of items and their respective values to find the optimal solution for the knapsack problem. **

The problem statement for a research paper on the knapsack problem in computer science would be to investigate and analyze the various algorithms and approaches for solving the knapsack problem. The paper would aim to address the challenge of finding the most efficient and effective algorithm for solving the knapsack problem, considering factors such as time complexity, space complexity, and practical applications. Additionally, the research paper would seek to explore potential improvements or optimizations to existing algorithms for solving the knapsack problem. **

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In the Knapsack problem, the number 11 in the table represents the maximum value that can be achieved with a knapsack of capacity 5 and a set of items with varying weights and values. To arrive at this number, dynamic programming techniques are typically used to fill in the table by considering the optimal value that can be obtained by including or excluding each item in the knapsack. By iteratively updating the table based on these considerations, the algorithm eventually arrives at the maximum value that can be achieved with the given constraints, which in this case is 11. **

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