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: "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.

Connected through hyperedge HE-1 (induced by source paper "Structural Similarity and Adaptation"):
  with members:
    - "Structural Similarity as Guidance in Case-Based Design"
    - "A Preprocessing Model for Integrating CBR and Prototype-Based Neural Networks"

Connected through hyperedge HE-2 (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"
    - "Conceptual Analogy: Conceptual clustering for informed and efficient analogical reasoning"
    - "Structural Similarity and Adaptation"
    - "S o l u t i o n Relevant A b s t r a"

Connected through hyperedge HE-3 (induced by source paper "Fish and Shrink. A next step towards e-cient case retrieval in large scaled case bases"):
  with members:
    (none)

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