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 Neural Networks to Identify Jets"
  Abstract: A neural network method for identifying the ancestor of a hadron jet is presented. The idea is to find an efficient mapping between certain observed hadronic kinematical variables and the quark/gluon identity. This is done with a neuronic expansion in terms of a network of sigmoidal functions using a gradient descent procedure, where the errors are back-propagated through the network. With this method we are able to separate gluon from quark jets originating from Monte Carlo generated e + e events with ~ 85% accuracy. The result is independent on the MC model used. This approach for isolating the gluon jet is then used to study the so-called string effect. In addition, heavy quarks (b and c) in e + e reactions can be identified on the 50% level by just observing the hadrons. In particular we are able to separate b-quarks with an efficiency and purity, which is comparable with what is expected from vertex detectors. We also speculate on how the neural network method can be used to disentangle different hadronization schemes by compressing the dimensionality of the state space of hadrons.

Connected through hyperedge HE-1 (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"
    - "LU TP 91-4 Self-organizing Networks for Extracting Jet Features"
    - "LU TP 91-25 Mass Reconstruction with a Neural Network"

Connected through hyperedge HE-2 (induced by source paper "LU TP 91-25 Mass Reconstruction with a Neural Network"):
  with members:
    - "LU TP 91-4 Self-organizing Networks for Extracting Jet Features"
    - "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-3 (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-4 (induced by source paper "LU TP 91-4 Self-organizing Networks for Extracting Jet Features"):
  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"

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