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: "accelerating natural gradient with higher order invariance"
  Abstract: An appealing property of the natural gradient is that it is invariant to arbitrary differentiable reparameterizations of the model. However, this invariance property requires infinitesimal steps and is lost in practical implementations with small but finite step sizes. In this paper, we study invariance properties from a combined perspective of Riemannian geometry and numerical differential equation solving. We define the order of invariance of a numerical method to be its convergence order to an invariant solution. We propose to use higher-order integrators and corrections based on geodesics to obtain more invariant optimization trajectories. We prove the numerical convergence properties of geodesic corrected updates and show that they can be as computational efficient as plain natural gradient. Experimentally, we demonstrate that invariance leads to faster training and our techniques improve on traditional natural gradient in optimizing synthetic objectives as well as deep classifiers and autoencoders.

Connected through hyperedge HE-1 (induced by source paper "ngboost natural gradient boosting for probabilistic prediction"):
  with members:
    - "countdown regression sharp and calibrated survival predictions"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "on calibration of modern neural networks"
    - "improving palliative care with deep learning"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "xgboost a scalable tree boosting system"
    - "new insights and perspectives on the natural gradient method"

Connected through hyperedge HE-2 (induced by source paper "a coordinate free construction of scalable natural gradient"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "noisy natural gradient as variational inference"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "trust region policy optimization"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "new insights and perspectives on the natural gradient method"
    - "riemannian metrics for neural networks i feedforward 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)