human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "NACODAE: Navy Conversational Decision Aids Environment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Case-Based Learning: Beyond Classification of Feature Vectors")
  Member "Context-Sensitive Feature Selection for Lazy Learners":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts")
  Member "Using Decision Trees to Improve Case-Based Learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning to Refine Case Libraries:")
  Member "Learning to Refine Case Libraries:":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Using Decision Trees to Improve Case-Based Learning")
  Member "Simplifying Decision Trees: A Survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "NACODAE: Navy Conversational Decision Aids Environment")
  Member "Correcting for Length Biasing in Conversational Case Scoring":
    also connected through other hyperedges:
      (none)
  Member "A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning")
  Member "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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