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: "A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner"
  Abstract: Conversational case-based reasoning (CCBR) is a form of interactive case-based reasoning where users input a partial problem description (in text). The CCBR system responds with a ranked solution display, which lists the solutions of stored cases whose problem descriptions best match the user's, and a ranked question display, which lists the unanswered questions in these cases. Users interact with these displays, either refining their problem description by answering selected questions, or selecting a solution to apply. CCBR systems should support dialogue inferencing; they should infer answers to questions that are implied by the problem description. Otherwise, questions will be listed that the user believes they have already answered. The standard approach to dialogue inferencing allows case library designers to insert rules that define implications between the problem description and unanswered questions. However, this approach imposes substantial knowledge engineering requirements. We introduce an alternative approach whereby an intelligent assistant guides the designer in defining a model of their case library, from which implication rules are derived. We detail this approach, its benefits, and explain how it can be supported through an integration with Parka-DB, a fast relational database system. We will evaluate our approach in the context of our CCBR system, named NaCoDAE. This paper appeared at the 1998 AAAI Spring Symposium on Multimodal Reasoning, and is NCARAI TR AIC-97-023. We introduce an integrated reasoning approach in which a model-based reasoning component performs an important inferencing role in a conversational case-based reasoning (CCBR) system named NaCoDAE (Breslow & Aha, 1997) (Figure 1). CCBR is a form of case-based reasoning where users enter text queries describing a problem and the system assists in eliciting refinements of it (Aha & Breslow, 1997). Cases 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"
    - "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning"

Connected through hyperedge HE-2 (induced by source paper "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning"):
  with members:
    - "Refining Conversational Case Libraries"
    - "A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms"

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