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: "on connected sublevel sets in deep learning"
  Abstract: This paper shows that every sublevel set of the loss function of a class of deep over-parameterized neural nets with piecewise linear activation functions is connected and unbounded. This implies that the loss has no bad local valleys and all of its global minima are connected within a unique and potentially very large global valley.

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"
    - "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"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

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"
    - "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"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-3 (induced by source paper "global convergence of deep networks with one wide layer followed by pyramidal topology"):
  with members:
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "neural networks learning and memorization with almost no over parameterization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-4 (induced by source paper "how much over parameterization is sufficient to learn deep relu networks"):
  with members:
    - "two models of double descent for weak features"
    - "beyond linearization on quadratic and higher order approximation of wide neural networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "over parameterized two level neural networks can learn near optimal feature representations"
    - "convex formulation of overparameterized deep neural networks"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"

Connected through hyperedge HE-5 (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 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"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "landscape connectivity and dropout stability of sgd solutions for over parameterized neural networks"):
  with members:
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "the state of sparsity in deep neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "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"
    - "understanding the loss surface of neural networks for binary classification"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-7 (induced by source paper "how noise affects the hessian spectrum in overparameterized neural networks"):
  with members:
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "gradient descent finds global minima of deep neural networks"
    - "three factors influencing minima in sgd"
    - "energy entropy competition and the effectiveness of stochastic gradient descent in machine learning"
    - "gradient descent happens in a tiny subspace"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"

Connected through hyperedge HE-8 (induced by source paper "explaining landscape connectivity of low cost solutions for multilayer nets"):
  with members:
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "understanding the loss surface of neural networks for binary classification"
    - "spurious local minima are common in two layer relu neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "the loss surfaces of multilayer networks"
    - "efficient object localization using convolutional networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

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)