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: "Technical Diagnosis: Fallexperte-D of further knowledge sources (domain knowledge, common knowledge) is investigated in the"
  Abstract: Case based reasoning (CBR) uses the knowledge from former experiences ("known cases"). Since special knowledge of an expert is mainly subject to his experiences, the CBR techniques are a good base for the development of expert systems. We investigate the problem for technical diagnosis. Diagnosis is not considered as a classification task, but as a process to be guided by computer assisted experience. This corresponds to the flexible "case completion" approach. Flexibility is also needed for the expert view with predominant interest in the unexpected, unpredictible cases.

Connected through hyperedge HE-1 (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"
    - "CBR on Semi-structured Documents: The ExperienceBook and the FAllQ Project"
    - "Case Retrieval Nets Applied to Large Case Bases"
    - "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