human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "communication efficient algorithms for distributed stochastic principal component analysis"):
  Abstract: We study the fundamental problem of Principal Component Analysis in a statistical distributed setting in which each machine out of $m$ stores a sample of $n$ points sampled i.i.d. from a single unknown distribution. We study algorithms for estimating the leading principal component of the population covariance matrix that are both communication-efficient and achieve estimation error of the order of the centralized ERM solution that uses all $mn$ samples. On the negative side, we show that in contrast to results obtained for distributed estimation under convexity assumptions, for the PCA objective, simply averaging the local ERM solutions cannot guarantee error that is consistent with the centralized ERM. We show that this unfortunate phenomena can be remedied by performing a simple correction step which correlates between the individual solutions, and provides an estimator that is consistent with the centralized ERM for sufficiently-large $n$. We also introduce an iterative distributed algorithm that is applicable in any regime of $n$, which is based on distributed matrix-vector products. The algorithm gives significant acceleration in terms of communication rounds over previous distributed algorithms, in a wide regime of parameters.

Includes members:
  Member "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "even faster svd decomposition yet without agonizing pain":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning for mixed linear regression")
  Member "convergence of stochastic gradient descent for pca":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exponentially convergent stochastic k pca without variance reduction")
  Member "the fast convergence of incremental pca":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "fast stochastic algorithms for svd and pca convergence properties and convexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "optimal principal component analysis in distributed and streaming models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "a stochastic pca and svd algorithm with an exponential convergence rate":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning a single neuron with gradient methods")
  Member "communication efficient distributed dual coordinate ascent":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic federated learning")

Question: Please tell me which class the source paper 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)