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: "Refining Conversational Case Libraries"
  Abstract: Conversational case-based reasoning (CBR) shells (e.g., Inference's CBR Express) are commercially successful tools for supporting the development of help desk and related applications. In contrast to rule-based expert systems, they capture knowledge as cases rather than more problematic rules, and they can be incrementally extended. However, rather than eliminate the knowledge engineering bottleneck, they refocus it on case engineering, the task of carefully authoring cases according to library design guidelines to ensure good performance. Designing complex libraries according to these guidelines is difficult; software is needed to assist users with case authoring. We describe an approach for revising case libraries according to design guidelines, its implementation in Clire, and empirical results showing that, under some conditions, this approach can improve conversational CBR performance.

Connected through hyperedge HE-1 (induced by source paper "Correcting for Length Biasing in Conversational Case Scoring"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Learning to Refine Case Libraries:"):
  with members:
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "Using Decision Trees to Improve Case-Based Learning"

Connected through hyperedge HE-3 (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"
    - "Learning to Refine Case Libraries:"
    - "Error-Correcting Output Coding Corrects Bias and Variance"
    - "Oblivious Decision Trees and Abstract Cases"

Connected through hyperedge HE-4 (induced by source paper "Simplifying Decision Trees: A Survey"):
  with members:
    - "NACODAE: Navy Conversational Decision Aids Environment"

Connected through hyperedge HE-5 (induced by source paper "A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner"):
  with members:
    - "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning"

Connected through hyperedge HE-6 (induced by source paper "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning"):
  with members:
    - "A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner"
    - "A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms"

Connected through hyperedge HE-7 (induced by source paper "NACODAE: Navy Conversational Decision Aids Environment"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "Case-Based Learning: Beyond Classification of Feature Vectors"
    - "Simplifying Decision Trees: A Survey"

Connected through hyperedge HE-8 (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"
    - "Learning to Refine 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