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: "In  Estimating analogical similarity by dot-products of Holographic Reduced Representations."
  Abstract: Models of analog retrieval require a computationally cheap method of estimating similarity between a probe and the candidates in a large pool of memory items. The vector dot-product operation would be ideal for this purpose if it were possible to encode complex structures as vector representations in such a way that the superficial similarity of vector representations reflected underlying structural similarity. This paper describes how such an encoding is provided by Holographic Reduced Representations (HRRs), which are a method for encoding nested relational structures as fixed-width distributed representations. The conditions under which structural similarity is reflected in the dot-product rankings of

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"
    - "Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts?"
    - "Making SME greedy and pragmatic"

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"

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: Neural_Networks