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: "fast stochastic algorithms for svd and pca convergence properties and convexity"
  Abstract: We study the convergence properties of the VR-PCA algorithm introduced by \cite{shamir2015stochastic} for fast computation of leading singular vectors. We prove several new results, including a formal analysis of a block version of the algorithm, and convergence from random initialization. We also make a few observations of independent interest, such as how pre-initializing with just a single exact power iteration can significantly improve the runtime of stochastic methods, and what are the convexity and non-convexity properties of the underlying optimization problem.

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 "exponentially convergent stochastic k pca without variance reduction"):
  with members:
    - "convergence of stochastic gradient descent for pca"
    - "the fast convergence of incremental pca"
    - "optimal and adaptive algorithms for online boosting"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "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-3 (induced by source paper "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"):
  with members:
    - "riemannian stochastic quasi newton algorithm with variance reduction and its convergence analysis"
    - "riemannian stochastic variance reduced gradient on grassmann manifold"
    - "sdca without duality"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "online identification and tracking of subspaces from highly incomplete information"

Connected through hyperedge HE-4 (induced by source paper "stochastic approximation algorithms for principal component analysis"):
  with members:
    - "the fast convergence of incremental pca"

Connected through hyperedge HE-5 (induced by source paper "tight query complexity lower bounds for pca via finite sample deformed wigner law"):
  with members:
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "lifting randomized query complexity to randomized communication complexity"
    - "how to escape saddle points efficiently"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "lower bounds for adaptive sparse recovery"

Connected through hyperedge HE-6 (induced by source paper "katyusha x practical momentum method for stochastic sum of nonconvex optimization"):
  with members:
    - "how to make the gradients small stochastically"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "sdca without duality regularization and individual convexity"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

Connected through hyperedge HE-7 (induced by source paper "averaging stochastic gradient descent on riemannian manifolds"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "convergence of stochastic gradient descent for pca"

Connected through hyperedge HE-8 (induced by source paper "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"):
  with members:
    - "undersampled sparse phase retrieval via majorization minimization"
    - "low rank phase retrieval"
    - "inexact alternating optimization for phase retrieval in the presence of outliers"
    - "sparse phase retrieval via truncated amplitude flow"
    - "non convex phase retrieval from stft measurements"
    - "phase retrieval using feasible point pursuit algorithms and cramer rao bound"
    - "phasemax convex phase retrieval via basis pursuit"
    - "guaranteed matrix completion via non convex 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)