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: "deep online convex optimization with gated games"
  Abstract: Methods from convex optimization are widely used as building blocks for deep learning algorithms. However, the reasons for their empirical success are unclear, since modern convolutional networks (convnets), incorporating rectifier units and max-pooling, are neither smooth nor convex. Standard guarantees therefore do not apply. This paper provides the first convergence rates for gradient descent on rectifier convnets. The proof utilizes the particular structure of rectifier networks which consists in binary active/inactive gates applied on top of an underlying linear network. The approach generalizes to max-pooling, dropout and maxout. In other words, to precisely the neural networks that perform best empirically. The key step is to introduce gated games, an extension of convex games with similar convergence properties that capture the gating function of rectifiers. The main result is that rectifier convnets converge to a critical point at a rate controlled by the gated-regret of the units in the network. Corollaries of the main result include: (i) a game-theoretic description of the representations learned by a neural network; (ii) a logarithmic-regret algorithm for training neural nets; and (iii) a formal setting for analyzing conditional computation in neural nets that can be applied to recently developed models of attention.

Connected through hyperedge HE-1 (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"
    - "neural taylor approximations convergence and exploration in rectifier networks"
    - "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-2 (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"
    - "neural taylor approximations convergence and exploration in rectifier networks"
    - "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-3 (induced by source paper "online learning with gated linear networks"):
  with members:
    - "introduction to online convex optimization"
    - "efficient second order online learning by sketching"
    - "an architecture for deep hierarchical generative models"
    - "compress and control"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "linear and geometric mixtures analysis"
    - "mixing strategies in data compression"
    - "context tree switching"

Connected through hyperedge HE-4 (induced by source paper "neural taylor approximations convergence and exploration in rectifier networks"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "noisy activation functions"
    - "convexified convolutional neural networks"
    - "deep residual learning for image recognition"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)