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 "convergence of stochastic gradient descent for pca"):
  Abstract: We consider the problem of principal component analysis (PCA) in a streaming stochastic setting, where our goal is to find a direction of approximate maximal variance, based on a stream of i.i.d. data points in $\reals^d$. A simple and computationally cheap algorithm for this is stochastic gradient descent (SGD), which incrementally updates its estimate based on each new data point. However, due to the non-convex nature of the problem, analyzing its performance has been a challenge. In particular, existing guarantees rely on a non-trivial eigengap assumption on the covariance matrix, which is intuitively unnecessary. In this paper, we provide (to the best of our knowledge) the first eigengap-free convergence guarantees for SGD in the context of PCA. This also partially resolves an open problem posed in \cite{hardt2014noisy}. Moreover, under an eigengap assumption, we show that the same techniques lead to new SGD convergence guarantees with better dependence on the eigengap.

Includes members:
  Member "pca with gaussian perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online learning via differential privacy")
  Member "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "smooth sensitivity based approach for differentially private principal component analysis")
  Member "the fast convergence of incremental pca":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "randomized block krylov methods for stronger and faster approximate singular value decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank")
  Member "global convergence of stochastic gradient descent for some non convex matrix problems":
    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 "the noisy power method a meta algorithm with applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")

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)