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: "neural taylor approximations convergence and exploration in rectifier networks"
  Abstract: Modern convolutional networks, incorporating rectifiers and max-pooling, are neither smooth nor convex; standard guarantees therefore do not apply. Nevertheless, methods from convex optimization such as gradient descent and Adam are widely used as building blocks for deep learning algorithms. This paper provides the first convergence guarantee applicable to modern convnets, which furthermore matches a lower bound for convex nonsmooth functions. The key technical tool is the neural Taylor approximation -- a straightforward application of Taylor expansions to neural networks -- and the associated Taylor loss. Experiments on a range of optimizers, layers, and tasks provide evidence that the analysis accurately captures the dynamics of neural optimization. The second half of the paper applies the Taylor approximation to isolate the main difficulty in training rectifier nets -- that gradients are shattered -- and investigates the hypothesis that, by exploring the space of activation configurations more thoroughly, adaptive optimizers such as RMSProp and Adam are able to converge to better solutions.

Connected through hyperedge HE-1 (induced by source paper "demc a deep dual encoder network for denoising monte carlo rendering"):
  with members:
    - "learning blind video temporal consistency"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "the shattered gradients problem if resnets are the answer then what is the question"):
  with members:
    - "diracnets training very deep neural networks without skip connections"
    - "skip connections as effective symmetry breaking"
    - "deep online convex optimization with gated games"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "benefits of depth in neural networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"

Connected through hyperedge HE-3 (induced by source paper "recovery guarantees for one hidden layer neural networks"):
  with members:
    - "relative error tensor low rank approximation"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "deep online convex optimization with gated games"
    - "reliably learning the relu in polynomial time"
    - "distribution specific hardness of learning neural networks"
    - "low rank approximation with entrywise ell_1 norm error"
    - "provable learning of noisy or networks"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-4 (induced by source paper "scalable planning with tensorflow for hybrid nonlinear domains"):
  with members:
    - "adadelta an adaptive learning rate 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)