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: "Extended Kalman filter in recurrent neural network training and pruning"
  Abstract: Recently, extended Kalman filter (EKF) based training has been demonstrated to be effective in neural network training. However, its conjunction with pruning methods such as weight decay and optimal brain damage (OBD) has not yet been studied. In this paper, we will elucidate the method of EKF training and propose a pruning method which is based on the results obtained by EKF training. These combined training pruning method is applied to a time series prediction problem.

Connected through hyperedge HE-1 (induced by source paper "Pruning with generalization based weight saliencies: flOBD, flOBS"):
  with members:
    (none)

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