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: "are deep resnets provably better than linear predictors"
  Abstract: Recently, a residual network (ResNet) with a single residual block has been shown to outperform linear predictors, in the sense that all its local minima are at least as good as the best linear predictor. We take a step towards extending this result to deep ResNets. As motivation, we first show that there exist datasets for which all local minima of a fully-connected ReLU network are no better than the best linear predictor, while a ResNet can have strictly better local minima. Second, we show that even at its global minimum, the representation obtained from the residual blocks of a 2-block ResNet does not necessarily improve monotonically as more blocks are added, highlighting a fundamental difficulty in analyzing deep ResNets. Our main result on deep ResNets shows that (under some geometric conditions) any critical point is either (i) at least as good as the best linear predictor; or (ii) the Hessian at this critical point has a strictly negative eigenvalue. Finally, we complement our results by analyzing near-identity regions of deep ResNets, obtaining size-independent upper bounds for the risk attained at critical points as well as the Rademacher complexity.

Connected through hyperedge HE-1 (induced by source paper "is supervised learning with adversarial features provably better than sole supervision"):
  with members:
    - "deep learning for image super resolution a survey"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "vital visual tracking via adversarial learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"
    - "image to image translation with conditional adversarial networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)