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: "The Structure-Mapping Engine: Algorithm and Examples"
  Abstract: This paper describes the Structure-Mapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the Structure-mapping theory and describe the design of the engine. We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper appeared in Artificial Intelligence, 41, 1989, pp 1-63. For more information, please contact forbus@ils.nwu.edu

Connected through hyperedge HE-1 (induced by source paper "Adapting Abstract Knowledge"):
  with members:
    (none)

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "The Role of Generic Models in Conceptual Change"):
  with members:
    - "Generic Teleological Mechanisms and their Use in Case Adaptation"
    - "Learning Generic Mechanisms from Experiences for Analogical Reasoning"

Connected through hyperedge HE-4 (induced by source paper "Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts?"):
  with members:
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "Making SME greedy and pragmatic"

Connected through hyperedge HE-5 (induced by source paper "Utilizing Prior Concepts for Learning"):
  with members:
    - "Evaluation and Selection of Biases in Machine Learning"
    - "An investigation of noise-tolerant relational concept learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "In  Estimating analogical similarity by dot-products of Holographic Reduced Representations."):
  with members:
    - "MAC/FAC: A Model of Similarity-based Retrieval"

Connected through hyperedge HE-7 (induced by source paper "The Case for Graph-Structured Representations"):
  with members:
    - "The case for cases: a call for purity in case-based reasoning inherently more difficult than"
    - "Massively Parallel Support for Case-based Planning"
    - "Massively Parallel Matching of Knowledge Structures"

Connected through hyperedge HE-8 (induced by source paper "Making SME greedy and pragmatic"):
  with members:
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "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