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: "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems"
  Abstract: We present a framework for task-driven knowledge acquisition in the development of design support systems. Different types of knowledge that enter the knowledge base of a design support system are defined and illustrated both from a formal and from a knowledge acquisition vantage point. Special emphasis is placed on the task-structure, which is used to guide both acquisition and application of knowledge. Starting with knowledge for planning steps in design and augmenting this with problem-solving knowledge that supports design, a formal integrated model of knowledge for design is constructed. Based on the notion of knowledge acquisition as an incremental process we give an account of possibilities for problem solving depending on the knowledge that is at the disposal of the system. Finally, we depict how different kinds of knowledge interact in a design support system. ? This research was supported by the German Ministry for Research and Technology (BMFT) within the joint project FABEL under contract no. 413-4001-01IW104. Project partners in FABEL are German National Research Center of Computer Science (GMD), Sankt Augustin, BSR Consulting GmbH, Munchen, Technical University of Dresden, HTWK Leipzig, University of Freiburg, and University of Karlsruhe.

Connected through hyperedge HE-1 (induced by source paper "Conceptual Analogy"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "Structural Similarity as Guidance in Case-Based Design"
    - "Towards Formalizations in Case-Based Reasoning for Synthesis"

Connected through hyperedge HE-2 (induced by source paper "Structural Similarity as Guidance in Case-Based Design"):
  with members:
    - "Conceptual Analogy"
    - "Towards Formalizations in Case-Based Reasoning for Synthesis"
    - "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 "MAC/FAC: A Model of Similarity-based Retrieval"):
  with members:
    - "Structural Similarity as Guidance in Case-Based Design"
    - "The Structure-Mapping Engine: Algorithm and Examples"
    - "Context-Based Similarity Applied to Retrieval of Relevant Cases"
    - "In  Estimating analogical similarity by dot-products of Holographic Reduced Representations."
    - "Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts?"
    - "Making SME greedy and pragmatic"

Connected through hyperedge HE-4 (induced by source paper "Towards Formalizations in Case-Based Reasoning for Synthesis"):
  with members:
    - "Conceptual Analogy"
    - "Structural Similarity as Guidance in Case-Based Design"
    - "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