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: "LU TP 91-4 Self-organizing Networks for Extracting Jet Features"
  Abstract: Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b,c and light quarks.

Connected through hyperedge HE-1 (induced by source paper "Using Neural Networks to Identify Jets"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "LU TP 90-3 Finding Gluon Jets with a Neural Trigger"
    - "LU TP 91-25 Mass Reconstruction with a Neural Network"

Connected through hyperedge HE-2 (induced by source paper "LU TP 90-3 Finding Gluon Jets with a Neural Trigger"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Using Neural Networks to Identify Jets"
    - "LU TP 91-25 Mass Reconstruction with a Neural Network"

Connected through hyperedge HE-3 (induced by source paper "LU TP 91-25 Mass Reconstruction with a Neural Network"):
  with members:
    - "Using Neural Networks to Identify Jets"
    - "LU TP 90-3 Finding Gluon Jets with a Neural Trigger"
    - "LU TP 93-13 On Langevin Updating in Multilayer Perceptrons"

Connected through hyperedge HE-4 (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