human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "Analysis and Empirical Studies of Derivational Analogy"
  Abstract: Derivational analogy is a technique for reusing problem solving experience to improve problem solving performance. This research addresses an issue common to all problem solvers that use derivational analogy: overcoming the mismatches between past experiences and new problems that impede reuse. First, this research describes the variety of mismatches that can arise and proposes a new approach to derivational analogy that uses appropriate adaptation strategies for each. Second, it compares this approach with seven others in a common domain. This empirical study shows that derivational analogy is almost always more efficient than problem solving from scratch, but the amount it contributes depends on its ability to overcome mismatches

Connected through hyperedge HE-1 (induced by source paper "Evaluating the Effectiveness of Derivation Replay in Partial-order vs State-space Planning"):
  with members:
    - "Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures"
    - "Design and Implementation of a Replay Framework based on a Partial Order Planner"
    - "An Explanation-Based Approach to Improve Retrieval in Case-Based Planning"
    - "Merge Strategies for Multiple Case Plan Replay"
    - "Automatic Storage and Indexing of Plan Derivations based on Replay Failures"

Question: Please tell me which class the center node belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Case_Based