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 "dissecting non vacuous generalization bounds based on the mean field approximation"):
  Abstract: Explaining how overparametrized neural networks simultaneously achieve low risk and zero empirical risk on benchmark datasets is an open problem. PAC-Bayes bounds optimized using variational inference (VI) have been recently proposed as a promising direction in obtaining non-vacuous bounds. We show empirically that this approach gives negligible gains when modeling the posterior as a Gaussian with diagonal covariance--known as the mean-field approximation. We investigate common explanations, such as the failure of VI due to problems in optimization or choosing a suboptimal prior. Our results suggest that investigating richer posteriors is the most promising direction forward.

Includes members:
  Member "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable deep neural networks via low rank matrix factorization")
  Member "hessian based analysis of sgd for deep nets dynamics and generalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "de randomized pac bayes margin bounds applications to non convex and non smooth predictors")
  Member "eigendamage structured pruning in the kronecker factored eigenbasis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "the role of invariance in spectral complexity based generalization bounds")
  Member "limitations of the empirical fisher approximation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scalable and practical natural gradient for large scale deep learning")
  Member "information theoretic local minima characterization and regularization":
    also connected through other hyperedges:
      (none)
  Member "the information complexity of learning tasks their structure and their distance":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting meta learning as supervised 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)