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: "Conceptual Analogy"
  Abstract: Conceptual analogy (CA) is an approach that integrates conceptualization, i.e., memory organization based on prior experiences and analogical reasoning (Borner 1994a). It was implemented prototypically and tested to support the design process in building engineering (Borner and Janetzko 1995, Borner 1995). There are a number of features that distinguish CA from standard approaches to CBR and AR. First of all, CA automatically extracts the knowledge needed to support design tasks (i.e., complex case representations, the relevance of object features and relations, and proper adaptations) from attribute-value representations of prior layouts. Secondly, it effectively determines the similarity of complex case representations in terms of adaptability. Thirdly, implemented and integrated into a highly interactive and adaptive system architecture it allows for incremental knowledge acquisition and user support. This paper surveys the basic assumptions and the psychological results which influenced the development of CA. It sketches the knowledge representation formalisms employed and characterizes the sub-processes needed to integrate memory organization and analogical reasoning.

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 "Structural Similarity as Guidance in Case-Based Design"):
  with members:
    - "Towards Formalizations in Case-Based Reasoning for Synthesis"
    - "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems"
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "Conceptual Analogy: Conceptual clustering for informed and efficient analogical reasoning"
    - "Structural Similarity and Adaptation"
    - "S o l u t i o n Relevant A b s t r a"
    - "Automatic Indexing, Retrieval and Reuse of Topologies in Architectual Layouts"

Connected through hyperedge HE-3 (induced by source paper "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems"):
  with members:
    - "Structural Similarity as Guidance in Case-Based Design"
    - "Towards Formalizations in Case-Based Reasoning for Synthesis"
    - "MAC/FAC: A Model of Similarity-based Retrieval"

Connected through hyperedge HE-4 (induced by source paper "Towards Formalizations in Case-Based Reasoning for Synthesis"):
  with members:
    - "Structural Similarity as Guidance in Case-Based Design"
    - "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems"
    - "Structure oriented case 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