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: "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
  Abstract: In current CBR systems, case adaptation is usually performed by rule-based methods that use task-specific rules hand-coded by the system developer. The ability to define those rules depends on knowledge of the task and domain that may not be available a priori, presenting a serious impediment to endowing CBR systems with the needed adaptation knowledge. This paper describes ongoing research on a method to address this problem by acquiring adaptation knowledge from experience. The method uses reasoning from scratch, based on introspective reasoning about the requirements for successful adaptation, to build up a library of adaptation cases that are stored for future reuse. We describe the tenets of the approach and the types of knowledge it requires. We sketch initial computer implementation, lessons learned, and open questions for further study.

Connected through hyperedge HE-1 (induced by source paper "Representing Self-knowledge for Introspection about Memory Search"):
  with members:
    - "Abstract"
    - "Abstract"
    - "Abstract"

Connected through hyperedge HE-2 (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 Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "On the Usefulness of Re-using Diagnostic Solutions"
    - "A Case Study of Case-Based CBR"
    - "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-3 (induced by source paper "Combining Rules and Cases to Learn Case Adaptation"):
  with members:
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Evaluating Computational Assistance for Crisis Response"
    - "Case-Based Planning to Learn"

Connected through hyperedge HE-4 (induced by source paper "Acquiring Case Adaptation Knowledge: A Hybrid Approach"):
  with members:
    - "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"
    - "A Case Study of Case-Based CBR"
    - "on Case-Based Reasoning Integrations Case-Based Seeding for an Interactive Crisis Response Assistant"

Connected through hyperedge HE-5 (induced by source paper "Towards A Computer Model of Memory Search Strategy Learning"):
  with members:
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Combining Rules and Cases to Learn Case Adaptation"
    - "Learning Adaptation Strategies by Introspective Reasoning about Memory Search"
    - "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"
    - "Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques"
    - "Goal-Driven Learning: Fundamental Issues (A Symposium Report)"

Connected through hyperedge HE-6 (induced by source paper "Towards Improving Case Adaptability with a Genetic Algorithm"):
  with members:
    - "Synergy and Commonality in Case-Based and Constraint-Based Reasoning"
    - "A CBR Integration From Inception to Productization"
    - "Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques"

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