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 Indices for Schema Selection"
  Abstract: In addition to learning new knowledge, a system must be able to learn when the knowledge is likely to be applicable. An index is a piece of information which, when identified in a given situation, triggers the relevant piece of knowledge (or schema) in the system's memory. We discuss the issue of how indices may be learned automatically in the context of a story understanding task, and present a program that can learn new indices for existing explanatory schemas. We discuss two methods using which the system can identify the relevant schema even if the input does not directly match an existing index, and learn a new index to allow it to retrieve this schema more efficiently in the future.

Connected through hyperedge HE-1 (induced by source paper "Incremental Learning of Explanation Patterns and their Indices"):
  with members:
    - "A theory of questions and question asking"
    - "Using Introspective Reasoning to Select Learning Strategies"
    - "Decision Models: A Theory of Volitional Explanation"
    - "A Goal-Based Approach to Intelligent Information Retrieval"

Connected through hyperedge HE-2 (induced by source paper "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"):
  with members:
    - "A theory of questions and question asking"
    - "Using Introspective Reasoning to Select Learning Strategies"
    - "Abstract"
    - "Model-Based Learning of Structural Indices to Design Cases"

Connected through hyperedge HE-3 (induced by source paper "Decision Models: A Theory of Volitional Explanation"):
  with members:
    - "A theory of questions and question asking"
    - "Using Introspective Reasoning to Select Learning Strategies"
    - "Incremental Learning of Explanation Patterns and their Indices"

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