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: "fast approximate natural gradient descent in a kronecker factored eigenbasis"
  Abstract: Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective descent directions. For models with many parameters, the covariance matrix they are based on becomes gigantic, making them inapplicable in their original form. This has motivated research into both simple diagonal approximations and more sophisticated factored approximations such as KFAC (Heskes, 2000; Martens & Grosse, 2015; Grosse & Martens, 2016). In the present work we draw inspiration from both to propose a novel approximation that is provably better than KFAC and amendable to cheap partial updates. It consists in tracking a diagonal variance, not in parameter coordinates, but in a Kronecker-factored eigenbasis, in which the diagonal approximation is likely to be more effective. Experiments show improvements over KFAC in optimization speed for several deep network architectures.

Connected through hyperedge HE-1 (induced by source paper "differential dynamic programming neural optimizer"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "feedback network for image super resolution"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "deep learning theory review an optimal control and dynamical systems perspective"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "hamiltonian neural networks"

Connected through hyperedge HE-2 (induced by source paper "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"):
  with members:
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "an empirical model of large batch training"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "eigenvalue corrected noisy natural gradient"
    - "three mechanisms of weight decay regularization"

Connected through hyperedge HE-3 (induced by source paper "eigendamage structured pruning in the kronecker factored eigenbasis"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "eigenvalue corrected noisy natural gradient"
    - "three mechanisms of weight decay regularization"
    - "rethinking the value of network pruning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "noisy natural gradient as variational inference"

Connected through hyperedge HE-4 (induced by source paper "a gram gauss newton method learning overparameterized deep neural networks for regression problems"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "on the variance of the adaptive learning rate and beyond"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "efficient subsampled gauss newton and natural gradient methods for training neural networks"
    - "fixup initialization residual learning without normalization"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"

Connected through hyperedge HE-5 (induced by source paper "discretizing continuous action space for on policy optimization"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "implicit policy for reinforcement learning"
    - "latent space policies for hierarchical reinforcement learning"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "scaling laws for the principled design initialization and preconditioning of relu networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "signsgd compressed optimisation for non convex problems"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "diagonal rescaling for neural networks"
    - "neural architecture search with reinforcement learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "limitations of the empirical fisher approximation for natural gradient descent"):
  with members:
    - "information matrices and generalization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "slang fast structured covariance approximations for bayesian deep learning with natural gradient"
    - "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)