human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Structural Similarity as Guidance in Case-Based Design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Conceptual Analogy")
  Member "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Conceptual Analogy")
  Member "The Structure-Mapping Engine: Algorithm and Examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Adapting Abstract Knowledge")
  Member "Context-Based Similarity Applied to Retrieval of Relevant Cases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "How to Retrieve Relevant Information?")
  Member "In  Estimating analogical similarity by dot-products of Holographic Reduced Representations.":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "The Structure-Mapping Engine: Algorithm and Examples")
  Member "Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts?":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "The Structure-Mapping Engine: Algorithm and Examples")
  Member "Making SME greedy and pragmatic":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "The Structure-Mapping Engine: Algorithm and Examples")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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