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: "Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning"
  Abstract: Acknowledgements: This research was supported in part by the Office of Naval Research (Cognitive and Neural Sciences, N00014-93-1-0208) and by the Whitaker Foundation (Special Opportunity Grant). We thank George Kalarickal, Charles Schmitt, William Ross, and Douglas Kelly for valuable discussions.

Connected through hyperedge HE-1 (induced by source paper "Using Sampling and Queries to Extract Rules from Trained Neural Networks"):
  with members:
    - "The Effective Size of a Neural Network: A Principal Component Approach"
    - "Extracting Provably Correct Rules from Artificial Neural Networks"
    - "Learning Symbolic Rules Using Artificial Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Rearrangement of receptive field topography after intracortical and peripheral stimulation: The role of plasticity in"):
  with members:
    - "Modeling dynamic receptive field changes in primary visual cortex using inhibitory learning"

Connected through hyperedge HE-3 (induced by source paper "An information-maximisation approach to blind separation and blind deconvolution"):
  with members:
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES"
    - "A New Learning Algorithm for Blind Signal Separation"
    - "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"
    - "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"
    - "Analyzing Hyperspectral Data with Independent Component Analysis"
    - "Independent Component Analysis of Electroencephalographic Data"

Connected through hyperedge HE-4 (induced by source paper "Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA"):
  with members:
    - "Modeling dynamic receptive field changes in primary visual cortex using inhibitory learning"
    - "In  Unsmearing Visual Motion: Development of Long-Range Horizontal Intrinsic Connections"
    - "Modeling dynamic receptive field changes produced by intracortical microstimulation"

Connected through hyperedge HE-5 (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"

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