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: "Conceptual Analogy: Conceptual clustering for informed and efficient analogical reasoning"
  Abstract: Conceptual analogy (CA) is a general approach that applies conceptual clustering and concept representations to facilitate the efficient use of past experiences (cases) during analogical reasoning (Borner 1995). The approach was developed and implemented in SYN* (see also (Borner 1994, Borner and Faauer 1995)) to support the design of supply nets in building engineering. This paper sketches the task; it outlines the nearest-neighbor-based agglomerative conceptual clustering applied in organizing large amounts of structured cases into case classes; it provides the concept representation used to characterize case classes and shows the analogous solution of new problems based on the concepts available. However, the main purpose of this paper is to evaluate CA in terms of its reasoning efficiency; its capability to derive solutions that go beyond the cases in the case base but still preserve the quality of cases.

Connected through hyperedge HE-1 (induced by source paper "Structural Similarity as Guidance in Case-Based Design"):
  with members:
    - "Conceptual Analogy"
    - "Towards Formalizations in Case-Based Reasoning for Synthesis"
    - "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems"
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "Structural Similarity and Adaptation"
    - "S o l u t i o n Relevant A b s t r a"
    - "Automatic Indexing, Retrieval and Reuse of Topologies in Architectual Layouts"

Connected through hyperedge HE-2 (induced by source paper "A Preprocessing Model for Integrating CBR and Prototype-Based Neural Networks"):
  with members:
    - "Structural Similarity and Adaptation"

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