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: "Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques"
  Abstract: The Dynamic Constraint Satisfaction Problem (DCSP) formalism has been gaining attention as a valuable and often necessary extension of the static CSP framework. Dynamic Constraint Satisfaction enables CSP techniques to be applied more extensively, since it can be applied in domains where the set of constraints and variables involved in the problem evolves with time. At the same time, the Case-Based Reasoning (CBR) community has been working on techniques by which to reuse existing solutions when solving new problems. We have observed that dynamic constraint satisfaction matches very closely the case-based reasoning process of case adaptation. These observations emerged from our previous work on combining CBR and CSP to achieve a constraint-based adaptation. This paper summarizes our previous results, describes the similarity of the challenges facing both DCSP and case adaptation, and shows how CSP and CBR can together begin to address these chal lenges.

Connected through hyperedge HE-1 (induced by source paper "Synergy and Commonality in Case-Based and Constraint-Based Reasoning"):
  with members:
    - "Towards Improving Case Adaptability with a Genetic Algorithm"

Connected through hyperedge HE-2 (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"
    - "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"
    - "Goal-Driven Learning: Fundamental Issues (A Symposium Report)"

Connected through hyperedge HE-3 (induced by source paper "Towards Improving Case Adaptability with a Genetic Algorithm"):
  with members:
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "Synergy and Commonality in Case-Based and Constraint-Based Reasoning"
    - "A CBR Integration From Inception to Productization"

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