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: "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.

Connected through hyperedge HE-1 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks"):
  with members:
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"
    - "gradient descent finds global minima of deep neural networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"

Connected through hyperedge HE-2 (induced by source paper "geometry and topology of deep neural networks decision boundaries"):
  with members:
    - "understanding global loss landscape of one hidden layer relu neural networks"
    - "barcodes as summary of objective function s topology"
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "on decision regions of narrow deep neural networks"
    - "learning one hidden layer neural networks under general input distributions"

Connected through hyperedge HE-3 (induced by source paper "revisiting landscape analysis in deep neural networks eliminating decreasing paths to infinity"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "elimination of all bad local minima in deep learning"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-4 (induced by source paper "sub optimal local minima exist for almost all over parameterized neural networks"):
  with members:
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-5 (induced by source paper "over parameterization as a catalyst for better generalization of deep relu network"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "luck matters understanding training dynamics of deep relu networks"
    - "measuring the intrinsic dimension of objective landscapes"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-6 (induced by source paper "student specialization in deep relu networks with finite width and input dimension"):
  with members:
    - "luck matters understanding training dynamics of deep relu networks"
    - "measuring the intrinsic dimension of objective landscapes"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "mass an accelerated stochastic method for over parametrized learning"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-7 (induced by source paper "are deep resnets provably better than linear predictors"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "understanding the loss surface of neural networks for binary classification"
    - "are resnets provably better than linear predictors"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-8 (induced by source paper "subgradient descent learns orthogonal dictionaries"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "rate optimal denoising with deep neural networks"
    - "nonconvex robust low rank matrix recovery"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "learning relu networks via alternating minimization"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"

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)