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: "Tempering Backpropagation Networks: Not All Weights are Created Equal approach yields hitherto unparalleled performance on"
  Abstract: Backpropagation learning algorithms typically collapse the network's structure into a single vector of weight parameters to be optimized. We suggest that their performance may be improved by utilizing the structural information instead of discarding it, and introduce a framework for tempering each weight accordingly. In the tempering model, activation and error signals are treated as approximately independent random variables. The characteristic scale of weight changes is then matched to that of the residuals, allowing structural properties such as a node's fan-in and fan-out to affect the local learning rate and backpropagated error. The model also permits calculation of an upper bound on the global learning rate for batch updates, which in turn leads to different update rules for bias vs. non-bias weights.

Connected through hyperedge HE-1 (induced by source paper "On Centering Neural Network Weight Updates"):
  with members:
    - "Feature Extraction Using an Unsupervised Neural Network"
    - "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain"

Connected through hyperedge HE-2 (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"
    - "Early Stopping but when?"

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