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: "Making SME greedy and pragmatic"
  Abstract: The Structure-Mapping Engine (SME) has successfully modeled several aspects of human consistent interpretations of an analogy. While useful for theoretical explorations, this aspect of the algorithm is both psychologically implausible and computationally inefficient. (2) SME contains no mechanism for focusing on interpretations relevant to an analogizer's goals. This paper describes modifications to SME which overcome these flaws. We describe a greedy merge algorithm which efficiently computes an approximate "best" interpretation, and can generate alternate interpretations when necessary. We describe pragmatic marking, a technique which focuses the mapping to produce relevant, yet novel, inferences. We illustrate these techniques via example and evaluate their performance using empirical data and theoretical analysis. analogical processing. However, it has two significant drawbacks: (1) SME constructs all structurally

Connected through hyperedge HE-1 (induced by source paper "MAC/FAC: A Model of Similarity-based Retrieval"):
  with members:
    - "Structural Similarity as Guidance in Case-Based Design"
    - "Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems"
    - "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?"

Connected through hyperedge HE-2 (induced by source paper "The Structure-Mapping Engine: Algorithm and Examples"):
  with members:
    - "Utilizing Prior Concepts for Learning"
    - "The Case for Graph-Structured Representations"
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts?"):
  with members:
    - "The Structure-Mapping Engine: Algorithm and Examples"
    - "MAC/FAC: A Model of Similarity-based 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