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: "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
  Abstract: The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very difficult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provides a framework for acquiring flexible adaptation knowledge from experiences with autonomous adaptation and suggests its potential as a basis for acquisition of adaptation knowledge from interactive user guidance. It also presents initial experimental results examining the benefits of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance.

Connected through hyperedge HE-1 (induced by source paper "A Case Study of Case-Based CBR"):
  with members:
    - "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "Case-Based Learning: Beyond Classification of Feature Vectors"

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"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "A Case Study of Case-Based CBR"
    - "Learning to Refine Indexing by Introspective Reasoning"

Connected through hyperedge HE-3 (induced by source paper "Combining Rules and Cases to Learn Case Adaptation"):
  with members:
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "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 "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"
    - "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "A Case Study of Case-Based CBR"

Connected through hyperedge HE-5 (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"
    - "Combining Rules and Cases to Learn Case Adaptation"
    - "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-6 (induced by source paper "on Case-Based Reasoning Integrations Case-Based Seeding for an Interactive Crisis Response Assistant"):
  with members:
    - "Learning to Predict User Operations for Adaptive Scheduling"

Connected through hyperedge HE-7 (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"
    - "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)"

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