human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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).

Includes members:
  Member "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping")
  Member "Centering Neural Network Gradient Factors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain")
  Member "Statistical Theory of Overtraining Is Cross-Validation Asymptotically Effective?":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Natural Gradient Descent for Training Multi-Layer Perceptrons")
  Member "Fast Pruning Using Principal Components":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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