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 "on the noisy gradient descent that generalizes as sgd"):
  Abstract: The gradient noise of SGD is considered to play a central role in the observed strong generalization abilities of deep learning. While past studies confirm that the magnitude and the covariance structure of gradient noise are critical for regularization, it remains unclear whether or not the class of noise distributions is important. In this work we provide negative results by showing that noises in classes different from the SGD noise can also effectively regularize gradient descent. Our finding is based on a novel observation on the structure of the SGD noise: it is the multiplication of the gradient matrix and a sampling noise that arises from the mini-batch sampling procedure. Moreover, the sampling noises unify two kinds of gradient regularizing noises that belong to the Gaussian class: the one using (scaled) Fisher as covariance and the one using the gradient covariance of SGD as covariance. Finally, thanks to the flexibility of choosing noise class, an algorithm is proposed to perform noisy gradient descent that generalizes well, the variant of which even benefits large batch SGD training without hurting generalization.

Includes members:
  Member "a tail index analysis of stochastic gradient noise in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unique properties of wide minima in deep networks")
  Member "hessian based analysis of sgd for deep nets dynamics and generalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation")
  Member "quasi potential as an implicit regularizer for the loss function in the stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the multiplicative noise in stochastic gradient descent data dependent regularization continuous and discrete approximation")
  Member "interplay between optimization and generalization of stochastic gradient descent with covariance noise":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "generalization bounds of stochastic gradient descent for wide and deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "three factors influencing minima in sgd":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "stochastic gradient descent performs variational inference converges to limit cycles for deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "opening the black box of deep neural networks via information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")

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)