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: "Priming, Perceptual Reversal, and Circular Reaction in a Neural Network Model of Schema-Based Vision"
  Abstract: VISOR is a neural network system for object recognition and scene analysis that learns visual schemas from examples. Processing in VISOR is based on cooperation, competition, and parallel bottom-up and top-down activation of schema representations. Similar principles appear to underlie much of human visual processing, and VISOR can therefore be used to model various perceptual phenomena. This paper focuses on analyzing three phenomena through simulation with VISOR: (1) priming and mental imagery, (2) perceptual reversal, and (3) circular reaction. The results illustrate similarity and subtle differences between the mechanisms mediating priming and mental imagery, show how the two opposing accounts of perceptual reversal (neural satiation and cognitive factors) may both contribute to the phenomenon, and demonstrate how intentional actions can be gradually learned from reflex actions. Successful simulation of such effects suggests that similar mechanisms may govern human visual perception and learning of visual schemas.

Connected through hyperedge HE-1 (induced by source paper "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"):
  with members:
    - "Back Propagation is Sensitive to Initial Conditions"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "VISOR: Schema-based Scene Analysis with Structured Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "VISOR: Schema-based Scene Analysis with Structured Neural Networks"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"

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