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: "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning"
  Abstract: Conversational case-based reasoning (CCBR) has been successfully used to assist in case retrieval tasks. However, behavioral limitations of CCBR motivate the search for integrations with other reasoning approaches. This paper briefly describes our group's ongoing efforts towards enhancing the inferencing behaviors of a conversational case-based reasoning development tool named NaCoDAE. In particular, we focus on integrating NaCoDAE with machine learning, model-based reasoning, and generative planning modules. This paper defines CCBR, briefly summarizes the integrations, and explains how they enhance the overall system. Our research focuses on enhancing the performance of conversational case-based reasoning (CCBR) systems (Aha & Breslow, 1997). CCBR is a form of case-based reasoning where users initiate problem solving conversations by entering an initial problem description in natural language text. This text is assumed to be a partial rather than a complete problem description. The CCBR system then assists in eliciting refinements of this description and in suggesting solutions. Its primary purpose is to provide a focus of attention for the user so as to quickly provide a solution(s) for their problem. Figure 1 summarizes the CCBR problem solving cycle. Cases in a CCBR library have three components:

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"
    - "Learning to Refine Case Libraries:"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner"):
  with members:
    - "Refining Conversational Case Libraries"

Connected through hyperedge HE-3 (induced by source paper "A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms"):
  with members:
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"
    - "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the"

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