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: "Combining Rules and Cases to Learn Case Adaptation"
  Abstract: Computer models of case-based reasoning (CBR) generally guide case adaptation using a fixed set of adaptation rules. A difficult practical problem is how to identify the knowledge required to guide adaptation for particular tasks. Likewise, an open issue for CBR as a cognitive model is how case adaptation knowledge is learned. We describe a new approach to acquiring case adaptation knowledge. In this approach, adaptation problems are initially solved by reasoning from scratch, using abstract rules about structural transformations and general memory search heuristics. Traces of the processing used for successful rule-based adaptation are stored as cases to enable future adaptation to be done by case-based reasoning. When similar adaptation problems are encountered in the future, these adaptation cases provide task- and domain-specific guidance for the case adaptation process. We present the tenets of the approach concerning the relationship between memory search and case adaptation, the memory search process, and the storage and reuse of cases representing adaptation episodes. These points are discussed in the context of ongoing research on DIAL, a computer model that learns case adaptation knowledge for case-based disaster response planning.

Connected through hyperedge HE-1 (induced by source paper "Case-Based Planning to Learn"):
  with members:
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"

Connected through hyperedge HE-2 (induced by source paper "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"):
  with members:
    - "Representing Self-knowledge for Introspection about Memory Search"
    - "Towards Improving Case Adaptability with a Genetic Algorithm"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"

Connected through hyperedge HE-3 (induced by source paper "Acquiring Case Adaptation Knowledge: A Hybrid Approach"):
  with members:
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "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-4 (induced by source paper "Evaluating Computational Assistance for Crisis Response"):
  with members:
    - "Learning to Predict User Operations for Adaptive Scheduling"

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"
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "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)"

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