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: "Early Stopping but when?"
  Abstract: Validation can be used to detect when overfitting starts during supervised training of a neural network; training is then stopped before convergence to avoid the overfitting ("early stopping"). The exact criterion used for validation-based early stopping, however, is usually chosen in an ad-hoc fashion or training is stopped interactively. This trick describes how to select a stopping criterion in a systematic fashion; it is a trick for either speeding learning procedures or improving generalization, whichever is more important in the particular situation. An empirical investigation on multi-layer perceptrons shows that there exists a tradeoff between training time and generalization: From the given mix of 1296 training runs using different 12 problems and 24 different network architectures I conclude slower stopping criteria allow for small improvements in generalization (here: about 4% on average), but cost much more training time (here: about factor 4 longer on average).

Connected through hyperedge HE-1 (induced by source paper "Fast Pruning Using Principal Components"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Statistical Theory of Overtraining Is Cross-Validation Asymptotically Effective?"):
  with members:
    - "Natural Gradient Descent for Training Multi-Layer Perceptrons"

Connected through hyperedge HE-3 (induced by source paper "Centering Neural Network Gradient Factors"):
  with members:
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"
    - "Tempering Backpropagation Networks: Not All Weights are Created Equal approach yields hitherto unparalleled performance on"

Connected through hyperedge HE-4 (induced by source paper "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Interpretable Neural Networks with BP-SOM"
    - "Avoiding Overfitting with BP-SOM"
    - "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"
    - "Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping"
    - "A BENCHMARK FOR CLASSIFIER LEARNING"
    - "Adaptive Parameter Pruning in Neural Networks"

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