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: Basic Ideas and Extensions"
  Abstract: An efficient retrieval of a relatively small number of relevant cases from a huge case base is a crucial subtask of Case-Based Reasoning. In this article, we present Case Retrieval Nets (CRNs), a memory model that has recently been developed for this task. The main idea is to apply a spreading activation process to a net-like case memory in order to retrieve cases being similar to a posed query case. We summarize the basic ideas of CRNs, suggest some useful extensions, and present some initial experimental results which suggest that CRNs can successfully handle case bases larger than considered usually in the CBR community.

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"
    - "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"
    - "An Investigation of Marker-Passing Algorithms for Analogue Retrieval"

Connected through hyperedge HE-2 (induced by source paper "Case Retrieval Nets Applied to Large Case Bases"):
  with members:
    - "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"

Connected through hyperedge HE-3 (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 Applied to Large Case Bases"
    - "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-4 (induced by source paper "Utilizing Connectionist Learning Procedures in Symbolic Case Retrieval Nets"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "CBR on Semi-structured Documents: The ExperienceBook and the FAllQ Project"):
  with members:
    - "CBR for Document Retrieval: The FAllQ Project"

Connected through hyperedge HE-6 (induced by source paper "CBR for Document Retrieval: The FAllQ Project"):
  with members:
    - "CBR on Semi-structured Documents: The ExperienceBook and the FAllQ Project"
    - "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"
    - "Justification Structures for Document Reuse"

Connected through hyperedge HE-7 (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"
    - "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"

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