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 "limitations of the empirical fisher approximation"):
  Abstract: Natural gradient descent, which preconditions a gradient descent update with the Fisher information matrix of the underlying statistical model, is a way to capture partial second-order information. Several highly visible works have advocated an approximation known as the empirical Fisher, drawing connections between approximate second-order methods and heuristics like Adam. We dispute this argument by showing that the empirical Fisher---unlike the Fisher---does not generally capture second-order information. We further argue that the conditions under which the empirical Fisher approaches the Fisher (and the Hessian) are unlikely to be met in practice, and that, even on simple optimization problems, the pathologies of the empirical Fisher can have undesirable effects.

Includes members:
  Member "interplay between optimization and generalization of stochastic gradient descent with covariance noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "fast approximate natural gradient descent in a kronecker factored eigenbasis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differential dynamic programming neural optimizer")
  Member "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable and practical natural gradient for large scale deep learning")
  Member "noisy natural gradient as variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable and practical natural gradient for large scale deep learning")
  Member "dissecting adam the sign magnitude and variance of stochastic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "laprop a better way to combine momentum with adaptive gradient")
  Member "optimizing neural networks with kronecker factored approximate curvature":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "new insights and perspectives on the natural gradient method":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an empirical study of large batch stochastic gradient descent with structured covariance noise")

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)