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: "An Investigation of Marker-Passing Algorithms for Analogue Retrieval"
  Abstract: If analogy and case-based reasoning systems are to scale up to very large case bases, it is important to analyze the various methods used for retrieving analogues to identify the features of the problem for which they are appropriate. This paper reports on one such analysis, a comparison of retrieval by marker passing or spreading activation in a semantic network with Knowledge-Directed Spreading Activation, a method developed to be well-suited for retrieving semantically distant analogues from a large knowledge base. The analysis has two complementary components: (1) a theoretical model of the retrieval time based on a number of problem characteristics, and (2) experiments showing how the retrieval time of the approaches varies with the knowledge base size. These two components, taken together, suggest that KDSA is more likely than SA to be able to scale up to retrieval in large knowledge bases.

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"
    - "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"

Connected through hyperedge HE-2 (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"
    - "CBR for Document Retrieval: The FAllQ Project"
    - "Case Retrieval Nets: Foundations, Properties,"

Connected through hyperedge HE-3 (induced by source paper "Finding Analogues for Innovative Design"):
  with members:
    - "Towards More Creative Case-Based Design Systems"
    - "CASE-BASED CREATIVE DESIGN"

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"
    - "CBR for Document Retrieval: The FAllQ Project"
    - "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