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 93-13 On Langevin Updating in Multilayer Perceptrons"
  Abstract: The Langevin updating rule, in which noise is added to the weights during learning, is presented and shown to improve learning on problems with initially ill-conditioned Hessians. This is particularly important for multilayer perceptrons with many hidden layers, that often have ill-conditioned Hessians. In addition, Manhattan updating is shown to have a similar effect.

Connected through hyperedge HE-1 (induced by source paper "LU TP 93-24 Predicting System Loads with Artificial Neural"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"

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"
    - "Using Neural Networks to Identify Jets"
    - "LU TP 90-3 Finding Gluon Jets with a Neural Trigger"

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