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: "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
  Abstract: Using scene analysis as the task, this research focuses on three fundamental problems in neural network systems: (1) limited processing resources, (2) representing schemas, and (3) learning schemas. The first problem arises because no practical neural network can process all the visual input simultaneously and efficiently. The solution is to process a small amount of the input in parallel, and successively focus on the other parts of the input. This strategy requires that the system maintains structured knowledge for describing and interpreting the gathered information. The system should also learn to represent structured knowledge from examples of objects and scenes. VISOR, the system described in this paper, consists of three main components. The Low-Level Visual Module (simulated using procedural programs) extracts featural and positional information from the visual input. The Schema Module encodes structured knowledge about possible objects, and provides top-down information for the Low-Level Visual Module to focus attention at different parts of the scene. The Response Module learns to associate the schema activation patterns with external responses. It enables the external environment to provide reinforcement feedback for the learning of schematic structures.

Connected through hyperedge HE-1 (induced by source paper "Back Propagation is Sensitive to Initial Conditions"):
  with members:
    - "Multiassociative Memory"
    - "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"
    - "Scaling-up RAAMs"
    - "Replicability of Neural Computing Experiments"
    - "Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity"
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Neural Network Implementation in SAS R Software"

Connected through hyperedge HE-2 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Induction of Multiscale Temporal Structure"
    - "Misclassification Minimization"

Connected through hyperedge HE-3 (induced by source paper "Priming, Perceptual Reversal, and Circular Reaction in a Neural Network Model of Schema-Based Vision"):
  with members:
    - "VISOR: Schema-based Scene Analysis with Structured Neural Networks"

Connected through hyperedge HE-4 (induced by source paper "VISOR: Schema-based Scene Analysis with Structured Neural Networks"):
  with members:
    - "Priming, Perceptual Reversal, and Circular Reaction in a Neural Network Model of Schema-Based Vision"

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