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: "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
  Abstract: Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

Connected through hyperedge HE-1 (induced by source paper "SaxEx a case-based reasoning system for generating expressive musical performances"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Conceptual Analogy"):
  with members:
    - "Structural Similarity as Guidance in Case-Based Design"
    - "Towards Formalizations in Case-Based Reasoning for Synthesis"
    - "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems"

Connected through hyperedge HE-3 (induced by source paper "A Formal Analysis of Case Base Retrieval"):
  with members:
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"
    - "The case for cases: a call for purity in case-based reasoning inherently more difficult than"
    - "Formalising the knowledge content of case memory systems"

Connected through hyperedge HE-4 (induced by source paper "Preparing Case Retrieval Nets for Distributed Processing"):
  with members:
    - "Case Retrieval Nets: Basic Ideas and Extensions"
    - "Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains"
    - "An Investigation of Marker-Passing Algorithms for Analogue Retrieval"

Connected through hyperedge HE-5 (induced by source paper "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"):
  with members:
    - "Dynamic Automatic Model Selection"
    - "Generalizing from Case Studies: A Case Study"
    - "Concept Learning and Flexible Weighting"

Connected through hyperedge HE-6 (induced by source paper "Computer-Supported Argumentation for Cooperative Design on the World-Wide Web"):
  with members:
    - "Cooperative Case-Based Reasoning"

Connected through hyperedge HE-7 (induced by source paper "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Teaching Strategy for Memory-Based Control"

Connected through hyperedge HE-8 (induced by source paper "Cooperative Case-Based Reasoning"):
  with members:
    - "Computer-Supported Argumentation for Cooperative Design on the World-Wide Web"

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