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 "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.

Includes members:
  Member "On Centering Neural Network Weight Updates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain")
  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")

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