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 "the multilinear structure of relu networks"):
  Abstract: We study the loss surface of neural networks equipped with a hinge loss criterion and ReLU or leaky ReLU nonlinearities. Any such network defines a piecewise multilinear form in parameter space, and as a consequence, optima of such networks generically occur in non-differentiable regions of parameter space. Any understanding of such networks must therefore carefully take into account their non-smooth nature. We show how to use techniques from nonsmooth analysis to study these non-differentiable loss surfaces. Our analysis focuses on three different scenarios: (1) a deep linear network with hinge loss and arbitrary data, (2) a one-hidden layer network with leaky ReLUs and linearly separable data, and (3) a one-hidden layer network with ReLU nonlinearities and linearly separable data. We show that all local minima are global minima in the first two scenarios. A bifurcation occurs when passing from the second to the the third scenario, in that ReLU networks do have non-optimal local minima. We provide a complete description of such sub-optimal solutions. We conclude by investigating the extent to which these phenomena do, or do not, persist when passing to the multiclass context.

Includes members:
  Member "learning relus via gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mean field analysis of two layer neural networks non asymptotic rates and generalization bounds")
  Member "deep linear neural networks with arbitrary loss all local minima are global":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "recovery guarantees for one hidden layer neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "the loss surface of deep and wide neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "translating diffusion wavelets and regularisation into residual networks")
  Member "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "sgd learns over parameterized networks that provably generalize on linearly separable data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the global convergence of training deep linear resnets")
  Member "when is a convolutional filter easy to learn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "convergence analysis of two layer neural networks with relu activation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "backward feature correction how deep learning performs 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)