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: "Concept Sharing: A Means to Improve Multi-Concept Learning"
  Abstract: This paper describes several means for sharing between related concepts to improve learning in the same domain. The sharing comes in the form of substructures or possibly entire structures of previous concepts which may aid in learning other concepts. These substructures highlight useful information in the domain. Using two domains, we evaluate the effectiveness of concept sharing with respect to accuracy, concept size, search complexity, and noise resistance.

Connected through hyperedge HE-1 (induced by source paper "The Structure-Mapping Engine: Algorithm and Examples"):
  with members:
    - "Utilizing Prior Concepts for Learning"
    - "The Case for Graph-Structured Representations"
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "The Role of Generic Models in Conceptual Change"
    - "Adapting Abstract Knowledge"
    - "Role of Stories 1"
    - "Is analogical problem solving always analogical? The case for imitation. Second draft Is analogical problem"
    - "Functional Programming by Analogy"

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