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 Case Study of Case-Based CBR"
  Abstract: Case-based reasoning depends on multiple knowledge sources beyond the case library, including knowledge about case adaptation and criteria for similarity assessment. Because hand coding this knowledge accounts for a large part of the knowledge acquisition burden for developing CBR systems, it is appealing to acquire it by learning, and CBR is a promising learning method to apply. This observation suggests developing case-based CBR systems, CBR systems whose components themselves use CBR. However, despite early interest in case-based approaches to CBR, this method has received comparatively little attention. Open questions include how case-based components of a CBR system should be designed, the amount of knowledge acquisition effort they require, and their effectiveness. This paper investigates these questions through a case study of issues addressed, methods used, and results achieved by a case-based planning system that uses CBR to guide its case adaptation and similarity assessment. The paper discusses design considerations and presents empirical results that support the usefulness of case-based CBR, that point to potential problems and tradeoffs, and that directly demonstrate the overlapping roles of different CBR knowledge sources. The paper closes with general lessons about case-based CBR and areas for future research.

Connected through hyperedge HE-1 (induced by source paper "Case-Based Learning: Beyond Classification of Feature Vectors"):
  with members:
    - "NACODAE: Navy Conversational Decision Aids Environment"

Connected through hyperedge HE-2 (induced by source paper "Case-Based Similarity Assessment: Estimating Adaptability from Experience"):
  with members:
    - "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "Learning to Refine Indexing by Introspective Reasoning"

Connected through hyperedge HE-3 (induced by source paper "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"):
  with members:
    - "S o l u t i o n Relevant A b s t r a"
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "On the Usefulness of Re-using Diagnostic Solutions"
    - "Merge Strategies for Multiple Case Plan Replay"
    - "Supporting Combined Human and Machine Planning: The Prodigy 4.0 User Interface Version 2.0*"
    - "Towards Mixed-Initiative Rationale-Supported Planning"

Connected through hyperedge HE-4 (induced by source paper "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"):
  with members:
    - "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"
    - "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"

Connected through hyperedge HE-5 (induced by source paper "Acquiring Case Adaptation Knowledge: A Hybrid Approach"):
  with members:
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "Combining Rules and Cases to Learn Case Adaptation"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "on Case-Based Reasoning Integrations Case-Based Seeding for an Interactive Crisis Response Assistant"

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