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: "information matrices and generalization"
  Abstract: This work revisits the use of information criteria to characterize the generalization of deep learning models. In particular, we empirically demonstrate the effectiveness of the Takeuchi information criterion (TIC), an extension of the Akaike information criterion (AIC) for misspecified models, in estimating the generalization gap, shedding light on why quantities such as the number of parameters cannot quantify generalization. The TIC depends on both the Hessian of the loss H and the covariance of the gradients C. By exploring the similarities and differences between these two matrices as well as the Fisher information matrix F, we study the interplay between noise and curvature in deep models. We also address the question of whether C is a reasonable approximation to F, as is commonly assumed.

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"
    - "limitations of the empirical fisher approximation"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "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 "the role of invariance in spectral complexity based generalization bounds"):
  with members:
    - "size free generalization bounds for convolutional neural networks"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "understanding generalization and optimization performance of deep cnns"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "exploring generalization in deep learning"

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"
    - "limitations of the empirical fisher approximation"
    - "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 "limitations of the empirical fisher approximation for natural gradient descent"):
  with members:
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "slang fast structured covariance approximations for bayesian deep learning with natural gradient"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "three factors influencing minima in sgd"
    - "signsgd compressed optimisation for non convex problems"
    - "noisy natural gradient as variational inference"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "understanding deep learning requires rethinking generalization"

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)