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: "Statistical Theory of Overtraining Is Cross-Validation Asymptotically Effective?"
  Abstract: A statistical theory for overtraining is proposed. The analysis treats realizable stochastic neural networks, trained with Kullback-Leibler loss in the asymptotic case. It is shown that the asymptotic gain in the generalization error is small if we perform early stopping, even if we have access to the optimal stopping time. Considering cross-validation stopping we answer the question: In what ratio the examples should be divided into training and testing sets in order to obtain the optimum performance. In the non-asymptotic region cross-validated early stopping always decreases the generalization error. Our large scale simulations done on a CM5 are in nice agreement with our analytical findings.

Connected through hyperedge HE-1 (induced by source paper "Natural Gradient Descent for Training Multi-Layer Perceptrons"):
  with members:
    - "Equivariant adaptive source separation"
    - "NIPS*97 The Efficiency and The Robustness of Natural Gradient Descent Learning Rule"

Connected through hyperedge HE-2 (induced by source paper "Early Stopping but when?"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "Centering Neural Network Gradient Factors"
    - "Fast Pruning Using Principal Components"

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