human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "fixup initialization residual learning without normalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers")
  Member "small nonlinearities in activation functions create bad local minima in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "resnet with one neuron hidden layers is a universal approximator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless compression of deep neural networks")
  Member "representing smooth functions as compositions of near identity functions with implications for deep network optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards understanding the importance of shortcut connections in residual networks")
  Member "on the loss landscape of a class of deep neural networks with no bad local valleys":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "understanding the loss surface of neural networks for binary classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "global convergence and geometric characterization of slow to fast weight evolution in neural network training for classifying linearly non separable data")
  Member "are resnets provably better than linear predictors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "exploring generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "overfitting in adversarially robust deep learning")

Question: Please tell me which class the source paper 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)