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: "new perspectives on the natural gradient method"
  Abstract: In this report we review and discuss some theoretical aspects of Amari's natural gradient method, provide a unifying picture of the many different versions of it which have appeared over the years, and offer some new insights and perspectives regarding the method and its relationship to other optimization methods. Among our various contributions is the identification of a general condition under which the Fisher information matrix and Schraudolph's generalized Gauss-Newton matrix are equivalent. This equivalence implies that optimization methods which use the latter matrix, such as the Hessian-free optimization approach of Martens, are actually natural gradient methods in disguise. It also lets us view natural gradient methods as approximate Newton methods, justifying the application of various "update damping" techniques to them, which are designed to compensate for break-downs in local quadratic approximations. Additionally, we analyze the parameterization invariance possessed by the natural gradient method in the idealized setting of infinitesimally small update steps, and consider the extent to which it holds for practical versions of the method which take large discrete steps. We go on to show that parameterization invariance is not possessed by the classical Newton-Raphson method (even in the idealized setting), and then give a general characterization of gradient-based methods which do possess it.

Connected through hyperedge HE-1 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "fault sneaking attack a stealthy framework for misleading deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "hessian aware zeroth order optimization for black box adversarial attack"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-2 (induced by source paper "layer wise conditioning analysis in exploring the learning dynamics of dnns"):
  with members:
    - "a mean field theory of batch normalization"
    - "mean field analysis of batch normalization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "group normalization"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "large scale gan training for high fidelity natural image synthesis"
    - "proximal backpropagation"

Connected through hyperedge HE-3 (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"
    - "limitations of the empirical fisher approximation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "task2vec task embedding for meta learning"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "fisher rao metric geometry and complexity of neural networks"
    - "emergence of invariance and disentanglement in deep representations"
    - "visualizing the loss landscape of neural nets"
    - "critical learning periods in deep neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "the inaturalist species classification and detection dataset"

Connected through hyperedge HE-5 (induced by source paper "measuring and regularizing networks in function space"):
  with members:
    - "continual learning through synaptic intelligence"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "gradient episodic memory for continual learning"
    - "regularizing neural networks by penalizing confident output distributions"
    - "icarl incremental classifier and representation learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "slang fast structured covariance approximations for bayesian deep learning with natural gradient"):
  with members:
    - "conjugate computation variational inference converting variational inference in non conjugate models to inferences in conjugate models"
    - "noisy natural gradient as variational inference"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "early stopping without a validation set"):
  with members:
    - "probabilistic line searches for stochastic optimization"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "towards a mathematical understanding of the difficulty in learning with feedforward neural networks"):
  with members:
    - "depth creates no bad local minima"
    - "the loss surface of deep and wide neural networks"
    - "on the depth of deep neural networks a theoretical view"
    - "adam a method for stochastic optimization"
    - "qualitatively characterizing neural network optimization problems"
    - "the loss surfaces of multilayer networks"
    - "new insights and perspectives on the natural gradient method"

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)