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: "global convergence of stochastic gradient descent for some non convex matrix problems"
  Abstract: Stochastic gradient descent (SGD) on a low-rank factorization is commonly employed to speed up matrix problems including matrix completion, subspace tracking, and SDP relaxation. In this paper, we exhibit a step size scheme for SGD on a low-rank least-squares problem, and we prove that, under broad sampling conditions, our method converges globally from a random starting point within $O(\epsilon^{-1} n \log n)$ steps with constant probability for constant-rank problems. Our modification of SGD relates it to stochastic power iteration. We also show experiments to illustrate the runtime and convergence of the algorithm.

Connected through hyperedge HE-1 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis"):
  with members:
    - "bandit principal component analysis"
    - "exponentially convergent stochastic k pca without variance reduction"
    - "an implicit form of krasulina s k pca update without the orthonormality constraint"
    - "on the regret minimization of nonconvex online gradient ascent for online pca"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"

Connected through hyperedge HE-2 (induced by source paper "no spurious local minima in deep quadratic networks"):
  with members:
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "polynomial regression as an alternative to neural nets"
    - "universal approximation with quadratic deep networks"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "convergence results for neural networks via electrodynamics"

Connected through hyperedge HE-3 (induced by source paper "exponentially convergent stochastic k pca without variance reduction"):
  with members:
    - "convergence of stochastic gradient descent for pca"
    - "the fast convergence of incremental pca"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "optimal and adaptive algorithms for online boosting"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "the noisy power method a meta algorithm with applications"
    - "matrix completion from a few entries"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "negative eigenvalues of the hessian in deep neural networks"):
  with members:
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "gradient descent happens in a tiny subspace"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"
    - "the loss surfaces of multilayer networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

Connected through hyperedge HE-5 (induced by source paper "moses a streaming algorithm for linear dimensionality reduction"):
  with members:
    - "robust frequent directions with application in online learning"
    - "what to expect when you are expecting on the grassmannian"
    - "fixed rank approximation of a positive semidefinite matrix from streaming data"
    - "streaming principal component analysis from incomplete data"
    - "weighted matrix completion and recovery with prior subspace information"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "learning the differential correlation matrix of a smooth function from point samples"

Connected through hyperedge HE-6 (induced by source paper "the convergence of stochastic gradient descent in asynchronous shared memory"):
  with members:
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "byzantine tolerant machine learning"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "deep residual learning for image recognition"
    - "staleness aware async sgd for distributed deep learning"
    - "taming the wild a unified analysis of hogwild style algorithms"

Connected through hyperedge HE-7 (induced by source paper "taming convergence for asynchronous stochastic gradient descent with unbounded delay in non convex learning"):
  with members:
    - "asynchronous stochastic gradient descent with variance reduction for non convex optimization"
    - "parallel matrix factorization for low rank tensor completion"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "parallel training of dnns with natural gradient and parameter averaging"

Connected through hyperedge HE-8 (induced by source paper "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"):
  with members:
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "sparse phase retrieval via truncated amplitude flow"
    - "non convex phase retrieval from stft measurements"
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "compressed sensing from phaseless gaussian measurements via linear programming in the natural parameter space"
    - "phasemax convex phase retrieval via basis pursuit"

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)