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: "MAC/FAC: A Model of Similarity-based Retrieval"
  Abstract: We present a model of similarity-based retrieval which attempts to capture three psychological phenomena: (1) people are extremely good at judging similarity and analogy when given items to compare. (2) Superficial remindings are much more frequent than structural remindings. (3) People sometimes experience and use purely structural analogical re-mindings. Our model, called MAC/FAC (for "many are called but few are chosen") consists of two stages. The first stage (MAC) uses a computationally cheap, non-structural matcher to filter candidates from a pool of memory items. That is, we redundantly encode structured representations as content vectors, whose dot product yields an estimate of how well the corresponding structural representations will match. The second stage (FAC) uses SME to compute a true structural match between the probe and output from the first stage. MAC/FAC has been fully implemented, and we show that it is capable of modeling patterns of access found in psychological data.

Connected through hyperedge HE-1 (induced by source paper "Context-Based Similarity Applied to Retrieval of Relevant Cases"):
  with members:
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "Constructive Similarity Assessment: Using Stored Cases to Define New Situations"
    - "How to Retrieve Relevant Information?"
    - "A Similarity-Based Retrieval Tool for Software Repositories"
    - "Applying Case-Based Reasoning to Control in Robotics"

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"
    - "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems"
    - "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 "The Structure-Mapping Engine: Algorithm and Examples"):
  with members:
    - "Utilizing Prior Concepts for Learning"
    - "The Case for Graph-Structured Representations"
    - "The Role of Generic Models in Conceptual Change"
    - "Adapting Abstract Knowledge"
    - "Role of Stories 1"
    - "Is analogical problem solving always analogical? The case for imitation. Second draft Is analogical problem"
    - "Functional Programming by Analogy"
    - "Modeling Analogical Problem Solving in a Production System Architecture"

Connected through hyperedge HE-4 (induced by source paper "Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts?"):
  with members:
    - "The Structure-Mapping Engine: Algorithm and Examples"
    - "Making SME greedy and pragmatic"

Connected through hyperedge HE-5 (induced by source paper "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems"):
  with members:
    - "Conceptual Analogy"
    - "Structural Similarity as Guidance in Case-Based Design"
    - "Towards Formalizations in Case-Based Reasoning for Synthesis"

Connected through hyperedge HE-6 (induced by source paper "In  Estimating analogical similarity by dot-products of Holographic Reduced Representations."):
  with members:
    - "The Structure-Mapping Engine: Algorithm and Examples"

Connected through hyperedge HE-7 (induced by source paper "Making SME greedy and pragmatic"):
  with members:
    - "The Structure-Mapping Engine: Algorithm and Examples"
    - "Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts?"

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