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: "Towards Improving Case Adaptability with a Genetic Algorithm"
  Abstract: Case combination is a difficult problem in Case Based Reasoning, as sub-cases often exhibit conflicts when merged together. In our previous work we formalized case combination by representing each case as a constraint satisfaction problem, and used the minimum conflicts algorithm to systematically synthesize the global solution. However, we also found instances of the problem in which the minimum conflicts algorithm does not perform case combination efficiently. In this paper we describe those situations in which initially retrieved cases are not easily adaptable, and propose a method by which to improve case adaptability with a genetic algorithm. We introduce a fitness function that maintains as much retrieved case information as possible, while also perturbing a sub-solution to allow subsequent case combination to proceed more efficiently.

Connected through hyperedge HE-1 (induced by source paper "Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques"):
  with members:
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Synergy and Commonality in Case-Based and Constraint-Based Reasoning"
    - "A CBR Integration From Inception to Productization"

Connected through hyperedge HE-2 (induced by source paper "Synergy and Commonality in Case-Based and Constraint-Based Reasoning"):
  with members:
    - "Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques"

Connected through hyperedge HE-3 (induced by source paper "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"):
  with members:
    - "Representing Self-knowledge for Introspection about Memory Search"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "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"

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