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: "how to escape saddle points efficiently"
  Abstract: This paper shows that a perturbed form of gradient descent converges to a second-order stationary point in a number iterations which depends only poly-logarithmically on dimension (i.e., it is almost "dimension-free"). The convergence rate of this procedure matches the well-known convergence rate of gradient descent to first-order stationary points, up to log factors. When all saddle points are non-degenerate, all second-order stationary points are local minima, and our result thus shows that perturbed gradient descent can escape saddle points almost for free. Our results can be directly applied to many machine learning applications, including deep learning. As a particular concrete example of such an application, we show that our results can be used directly to establish sharp global convergence rates for matrix factorization. Our results rely on a novel characterization of the geometry around saddle points, which may be of independent interest to the non-convex optimization community.

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 "an empirical study of large batch stochastic gradient descent with structured covariance noise"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "understanding short horizon bias in stochastic meta optimization"
    - "a coordinate free construction of scalable natural gradient"
    - "three factors influencing minima in sgd"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "measuring the effects of data parallelism on neural network training"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"

Connected through hyperedge HE-3 (induced by source paper "stochasticity of deterministic gradient descent large learning rate for multiscale objective function"):
  with members:
    - "variational optimization on lie groups with examples of leading generalized eigenvalue problems"
    - "analysis of momentum methods"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-4 (induced by source paper "on gradient based learning in continuous games"):
  with members:
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "bandit learning in concave n person games"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "a generic approach for escaping saddle points"
    - "cycles in adversarial regularized learning"
    - "improved training of wasserstein gans"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"

Connected through hyperedge HE-5 (induced by source paper "provable meta learning of linear representations"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "online meta learning"
    - "multi task deep neural networks for natural language understanding"
    - "cloze driven pretraining of self attention networks"
    - "provable guarantees for gradient based meta learning"
    - "characterizing and avoiding negative transfer"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-6 (induced by source paper "few shot learning via learning the representation provably"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "meta learning with differentiable convex optimization"
    - "online meta learning"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "meta learning with differentiable closed form solvers"

Connected through hyperedge HE-7 (induced by source paper "a hybrid model based and data driven approach to spectrum sharing in mmwave cellular networks"):
  with members:
    - "modeling infrastructure sharing in mmwave networks with shared spectrum licenses"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "spectrum sharing in mmwave cellular networks via cell association coordination and beamforming"
    - "sum rate maximization in sub 28 ghz millimeter wave mimo interfering networks"
    - "stochastic geometry modeling and analysis of multi tier millimeter wave cellular networks"
    - "the transitional behavior of interference in millimeter wave networks and its impact on medium access control"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "convergence to second order stationarity for non negative matrix factorization provably and concurrently"):
  with members:
    - "matrix completion has no spurious local minimum"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "descent methods for nonnegative matrix factorization"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"

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)