human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a pac bayesian approach to spectrally normalized margin bounds for neural networks"):
  Abstract: We present a generalization bound for feedforward neural networks in terms of the product of the spectral norm of the layers and the Frobenius norm of the weights. The generalization bound is derived using a PAC-Bayes analysis.

Includes members:
  Member "exploring generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "spectrally normalized margin bounds for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the generalization of bayesian deep nets for multi class classification")
  Member "on large batch training for deep learning generalization gap and sharp minima":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "norm based capacity control in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep conditioning treatment of neural networks")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)