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: "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"
  Abstract: This paper presents Objectdirected Case Retrieval Nets, a memory model developed for an application of Case-Based Reasoning to the task of technical diagnosis. The key idea is to store cases, i.e. observed symptoms and diagnoses, in a network and to enhance this network with an object model encoding knowledge about the devices in the application domain.

Connected through hyperedge HE-1 (induced by source paper "Preparing Case Retrieval Nets for Distributed Processing"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "Case Retrieval Nets: Basic Ideas and Extensions"
    - "An Investigation of Marker-Passing Algorithms for Analogue Retrieval"

Connected through hyperedge HE-2 (induced by source paper "Case Retrieval Nets: Foundations, Properties,"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Case Retrieval Nets Applied to Large Case Bases"):
  with members:
    - "Case Retrieval Nets: Basic Ideas and Extensions"

Connected through hyperedge HE-4 (induced by source paper "CBR for Document Retrieval: The FAllQ Project"):
  with members:
    - "Case Retrieval Nets: Basic Ideas and Extensions"
    - "CBR on Semi-structured Documents: The ExperienceBook and the FAllQ Project"
    - "Justification Structures for Document Reuse"

Connected through hyperedge HE-5 (induced by source paper "An Investigation of Marker-Passing Algorithms for Analogue Retrieval"):
  with members:
    - "Finding Analogues for Innovative Design"
    - "Preparing Case Retrieval Nets for Distributed Processing"
    - "Case Retrieval Nets: Basic Ideas and Extensions"

Connected through hyperedge HE-6 (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"
    - "CBR for Document Retrieval: The FAllQ Project"
    - "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