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 "Centering Neural Network Gradient Factors"):
  Abstract: Technical Report IDSIA-19-97 Abstract. It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals [2]. Here we generalize this notion to all factors involved in the network's gradient, leading us to propose centering the slope of hidden unit activation functions as well. Slope centering removes the linear component of backpropagated error; this improves credit assignment in networks with shortcut connections. Benchmark results show that this can speed up learning significantly without adversely affecting the trained network's generalization ability.

Includes members:
  Member "Feature Extraction Using an Unsupervised Neural Network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain")
  Member "Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Feature Extraction Using an Unsupervised Neural Network")
  Member "Early Stopping but when?":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Fast Pruning Using Principal Components")
  Member "Tempering Backpropagation Networks: Not All Weights are Created Equal approach yields hitherto unparalleled performance on":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "On Centering Neural Network Weight Updates")

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