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: "Using Many-Particle Decomposition to get a Parallel Self-Organising Map"
  Abstract: We propose a method for decreasing the computational complexity of self-organising maps. The method uses a partitioning of the neurons into disjoint clusters. Teaching of the neurons occurs on a cluster-basis instead of on a neuron-basis. For teaching an N-neuron network with N 0 samples, the computational complexity decreases from O(N 0 N) to O(N 0 log N). Furthermore, we introduce a measure for the amount of order in a self-organising map, and show that the introduced algorithm behaves as well as the original algorithm.

Connected through hyperedge HE-1 (induced by source paper "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"):
  with members:
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS"
    - "SELF-ORGANIZING PROCESS BASED ON LATERAL INHIBITION AND SYNAPTIC RESOURCE REDISTRIBUTION"
    - "IEEE Learning the Semantic Similarity of Reusable Software Components"
    - "Self-Organization and Segmentation in a Laterally Connected Orientation Map of Spiking Neurons"

Connected through hyperedge HE-2 (induced by source paper "Approximation with neural networks: Between local and global approximation"):
  with members:
    - "Constructive Training Methods for Feedforward Neural Networks with Binary Weights"

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