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 Case Libraries:"
  Abstract: Initial Results Abstract. Conversational case-based reasoning (CBR) systems, which incrementally extract a query description through a user-directed conversation, are advertised for their ease of use. However, designing large case libraries that have good performance (i.e., precision and querying efficiency) is difficult. CBR vendors provide guidelines for designing these libraries manually, but the guidelines are difficult to apply. We describe an automated inductive approach that revises conversational case libraries to increase their conformance with design guidelines. Revision increased performance on three conversational case libraries.

Connected through hyperedge HE-1 (induced by source paper "Refining Conversational Case Libraries"):
  with members:
    - "NACODAE: Navy Conversational Decision Aids Environment"
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "Using Decision Trees to Improve Case-Based Learning"
    - "Simplifying Decision Trees: A Survey"
    - "Correcting for Length Biasing in Conversational Case Scoring"
    - "A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner"
    - "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning"

Connected through hyperedge HE-2 (induced by source paper "Using Decision Trees to Improve Case-Based Learning"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Case-Based Sonogram Classification"
    - "Error-Correcting Output Coding Corrects Bias and Variance"
    - "Oblivious Decision Trees and Abstract Cases"
    - "Refining Conversational Case Libraries"

Connected through hyperedge HE-3 (induced by source paper "Context-Sensitive Feature Selection for Lazy Learners"):
  with members:
    - "ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts"
    - "Refining Conversational Case Libraries"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Context-sensitive attribute estimation in regression"
    - "The Management of Context-Sensitive Features: A Review of Strategies"

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