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: "Preparing Case Retrieval Nets for Distributed Processing"
  Abstract: In this paper, we discuss two approaches of applying the memory model of Case Retrieval Nets to applications where a distributed processing of information is required. For this, we distinguish two types of such applications, namely (a) the case of distributed case libraries and (b) the case of distributed cases. While a solution to the former is straightforward, the latter requires an extension to Case Retrieval Nets which provides a kind of partitioning of the entire net structure. This extended model even allows for a concurrent implementation of the retrieval process or for the use of collaborative agents for retrieval. Keywords: Case-based reasoning, case retrieval, memory structures, distributed processing.

Connected through hyperedge HE-1 (induced by source paper "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"):
  with members:
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Computer-Supported Argumentation for Cooperative Design on the World-Wide Web"
    - "Lazy Acquisition of Place Knowledge"
    - "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"
    - "SaxEx a case-based reasoning system for generating expressive musical performances"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
    - "NACODAE: Navy Conversational Decision Aids Environment"

Connected through hyperedge HE-2 (induced by source paper "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"):
  with members:
    - "Case Retrieval Nets: Basic Ideas and Extensions"
    - "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-3 (induced by source paper "An Investigation of Marker-Passing Algorithms for Analogue Retrieval"):
  with members:
    - "Finding Analogues for Innovative Design"
    - "Case Retrieval Nets: Basic Ideas and Extensions"
    - "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"

Connected through hyperedge HE-4 (induced by source paper "Case Retrieval Nets: Basic Ideas and Extensions"):
  with members:
    - "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"
    - "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