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 insights and perspectives on the natural gradient method"
  Abstract: Natural gradient descent is an optimization method traditionally motivated from the perspective of information geometry, and works well for many applications as an alternative to stochastic gradient descent. In this paper we critically analyze this method and its properties, and show how it can be viewed as a type of approximate 2nd-order optimization method, where the Fisher information matrix can be viewed as an approximation of the Hessian. This perspective turns out to have significant implications for how to design a practical and robust version of the method. Additionally, we make the following contributions to the understanding of natural gradient and 2nd-order methods: a thorough analysis of the convergence speed of stochastic natural gradient descent (and more general stochastic 2nd-order methods) as applied to convex quadratics, a critical examination of the oft-used "empirical" approximation of the Fisher matrix, and an analysis of the (approximate) parameterization invariance property possessed by natural gradient methods, which we show still holds for certain choices of the curvature matrix other than the Fisher, but notably not the Hessian.

Connected through hyperedge HE-1 (induced by source paper "an empirical study of large batch stochastic gradient descent with structured covariance noise"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "understanding short horizon bias in stochastic meta optimization"
    - "a coordinate free construction of scalable natural gradient"
    - "three factors influencing minima in sgd"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "measuring the effects of data parallelism on neural network training"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"

Connected through hyperedge HE-2 (induced by source paper "kalman meets bellman improving policy evaluation through value tracking"):
  with members:
    - "a kernel loss for solving the bellman equation"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "successor uncertainties exploration and uncertainty in temporal difference learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shallow updates for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "the uncertainty bellman equation and exploration"

Connected through hyperedge HE-3 (induced by source paper "forgetting outside the box scrubbing deep networks of information accessible from input output observations"):
  with members:
    - "machine unlearning linear filtration for logit based classifiers"
    - "thieves on sesame street model extraction of bert based apis"
    - "where is the information in a deep neural network"
    - "eternal sunshine of the spotless net selective forgetting in deep networks"
    - "enhanced convolutional neural tangent kernels"
    - "on exact computation with an infinitely wide neural net"
    - "certified data removal from machine learning models"
    - "information in infinite ensembles of infinitely wide neural networks"

Connected through hyperedge HE-4 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-5 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "continual learning a comparative study on how to defy forgetting in classification tasks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-7 (induced by source paper "hessian based analysis of sgd for deep nets dynamics and generalization"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "on the convergence of adam and beyond"
    - "positively scale invariant flatness of relu neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"

Connected through hyperedge HE-8 (induced by source paper "general probabilistic surface optimization and log density estimation"):
  with members:
    - "deep pdf probabilistic surface optimization and density estimation"
    - "spectral normalization for generative adversarial networks"
    - "understanding the effectiveness of lipschitz continuity in generative adversarial nets"
    - "mine mutual information neural estimation"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "improved training of wasserstein gans"

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)