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: "Case Retrieval Nets Applied to Large Case Bases"
  Abstract: This article presents some experimental results obtained from applying the Case Retrieval Net approach to large case bases. The obtained results suggest that CRNs can successfully handle case bases larger than considered in other reports.

Connected through hyperedge HE-1 (induced by source paper "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"):
  with members:
    - "Preparing Case Retrieval Nets for Distributed Processing"
    - "Case Retrieval Nets: Basic Ideas and Extensions"
    - "CBR for Document Retrieval: The FAllQ Project"
    - "An Investigation of Marker-Passing Algorithms for Analogue Retrieval"
    - "Case Retrieval Nets: Foundations, Properties,"

Connected through hyperedge HE-2 (induced by source paper "Case Retrieval Nets: Basic Ideas and Extensions"):
  with members:
    - "Preparing Case Retrieval Nets for Distributed Processing"
    - "Utilizing Connectionist Learning Procedures in Symbolic Case Retrieval Nets"
    - "Technical Diagnosis: Fallexperte-D of further knowledge sources (domain knowledge, common knowledge) is investigated in the"
    - "CBR on Semi-structured Documents: The ExperienceBook and the FAllQ Project"
    - "CBR for Document Retrieval: The FAllQ Project"
    - "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"
    - "An Investigation of Marker-Passing Algorithms for Analogue Retrieval"

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