human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "scalable and practical natural gradient for large scale deep learning"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "information matrices and generalization"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "image classification at supercomputer scale"
    - "don t use large mini batches use local sgd"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-2 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation"):
  with members:
    - "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors"
    - "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "information theoretic local minima characterization and regularization"
    - "the information complexity of learning tasks their structure and their distance"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"

Connected through hyperedge HE-3 (induced by source paper "better pac bayes bounds for deep neural networks using the loss curvature"):
  with members:
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "information matrices and generalization"
    - "the information complexity of learning tasks their structure and their distance"
    - "some limitations of norm based generalization bounds in deep neural networks"
    - "data dependent pac bayes priors via differential privacy"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"
    - "fisher rao metric geometry and complexity of neural networks"

Connected through hyperedge HE-4 (induced by source paper "backpack packing more into backprop"):
  with members:
    - "a modular approach to block diagonal hessian approximations for second order optimization methods"
    - "deepobs a deep learning optimizer benchmark suite"
    - "don t unroll adjoint differentiating ssa form programs"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "not all samples are created equal deep learning with importance sampling"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "coupling adaptive batch sizes with learning rates"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "information matrices and generalization"):
  with members:
    - "real numbers data science and chaos how to fit any dataset with a single parameter"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "three factors influencing minima in sgd"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "exploring generalization in deep learning"
    - "fisher rao metric geometry and complexity of neural networks"
    - "implicit regularization in deep learning"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"

Question: Please tell me which class the center node 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)