human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Automatic Indexing, Retrieval and Reuse of Topologies in Architectual Layouts"):
  Abstract: Former layouts contain much of the know-how of architects. A generic and automatic way to formalize this know-how in order to use it by a computer would save a lot of effort and money. However, there seems to be no such way. The only access to the know-how are the layouts themselves. Developing a generic software tool to reuse former layouts you cannot consider every part of the architectual domain or things like personal style. Tools used today only consider small parts of the architectual domain. Any personal style is ignored. Isn't it possible to build a basic tool which is adjusted by the content of the former layouts, but may be extended incremently by modeling as much of the domain as desirable? This paper will describe a reuse tool to perform this task focusing on topological and geometrical binary relations.

Includes members:
  Member "Structural Similarity as Guidance in Case-Based Design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Conceptual Analogy")
  Member "Structural Similarity and Adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Structural Similarity as Guidance in Case-Based Design")
  Member "Fish and Shrink. A next step towards e-cient case retrieval in large scaled case bases":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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