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: "Learning to Refine Indexing by Introspective Reasoning"
  Abstract: A significant problem for case-based reasoning (CBR) systems is deciding what features to use in judging case similarity for retrieval. We describe research that addresses the feature selection problem by using introspective reasoning to learn new features for indexing. Our method augments the CBR system with an introspective reasoning component which monitors system performance to detect poor retrievals, identifies features which would lead to retrieving cases requiring less adaptation, and refines the indices to include such features in order to avoid similar future failures. We explore the benefit of introspective reasoning by performing empirical tests on the implemented system. These tests examine the benefits of introspective index refinement and the effects of problem order on case and index learning, and show that introspective learning of new index features improves overall performance across the range of different problem orders.

Connected through hyperedge HE-1 (induced by source paper "Case-Based Similarity Assessment: Estimating Adaptability from Experience"):
  with members:
    - "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "A Case Study of Case-Based CBR"

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