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: "CBR for Document Retrieval: The FAllQ Project"
  Abstract: This paper reports about a project on document retrieval in an industrial setting. The objective is to provide a tool that helps finding documents related to a given query, such as answers in Frequently Asked Questions databases. A CBR approach has been used to develop a running prototypical system which is currently under practical evaluation.

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"
    - "Case Retrieval Nets Applied to Large Case Bases"
    - "An Investigation of Marker-Passing Algorithms for Analogue Retrieval"
    - "Case Retrieval Nets: Foundations, Properties,"

Connected through hyperedge HE-2 (induced by source paper "CBR on Semi-structured Documents: The ExperienceBook and the FAllQ Project"):
  with members:
    - "Case Retrieval Nets: Basic Ideas and Extensions"

Connected through hyperedge HE-3 (induced by source paper "Justification Structures for Document Reuse"):
  with members:
    (none)

Connected through hyperedge HE-4 (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"
    - "Case Retrieval Nets Applied to Large Case Bases"
    - "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