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: "Pruning with generalization based weight saliencies: flOBD, flOBS"
  Abstract: The purpose of most architecture optimization schemes is to improve generalization. In this presentation we suggest to estimate the weight saliency as the associated change in generalization error if the weight is pruned. We detail the implementation of both an O(N )-storage scheme extending OBD, as well as an O(N 2 ) scheme extending OBS. We illustrate the viability of the approach on pre diction of a chaotic time series.

Connected through hyperedge HE-1 (induced by source paper "Extended Kalman filter in recurrent neural network training and pruning"):
  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