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 "better pac bayes bounds for deep neural networks using the loss curvature"):
  Abstract: We investigate whether it's possible to tighten PAC-Bayes bounds for deep neural networks by utilizing the Hessian of the training loss at the minimum. For the case of Gaussian priors and posteriors we introduce a Hessian-based method to obtain tighter PAC-Bayes bounds that relies on closed form solutions of layerwise subproblems. We thus avoid commonly used variational inference techniques which can be difficult to implement and time consuming for modern deep architectures. Through careful experiments we analyze the influence of the prior mean, prior covariance, posterior mean and posterior covariance on obtaining tighter bounds. We discuss several limitations in further improving PAC-Bayes bounds through more informative priors.

Includes members:
  Member "eigendamage structured pruning in the kronecker factored eigenbasis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "picking winning tickets before training by preserving gradient flow")
  Member "uniform convergence may be unable to explain generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation")
  Member "limitations of the empirical fisher approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable and practical natural gradient for large scale deep learning")
  Member "information matrices and generalization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable and practical natural gradient for large scale deep learning")
  Member "the information complexity of learning tasks their structure and their distance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting meta learning as supervised learning")
  Member "some limitations of norm based generalization bounds in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation")
  Member "data dependent pac bayes priors via differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation")
  Member "fixing variational bayes deterministic variational inference for bayesian neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a simple baseline for bayesian uncertainty in deep learning")

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)