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 "globally convergent stochastic optimization for canonical correlation analysis"):
  Abstract: We study the stochastic optimization of canonical correlation analysis (CCA), whose objective is nonconvex and does not decouple over training samples. Although several stochastic optimization algorithms have been recently proposed to solve this problem, no global convergence guarantee was provided by any of them. Based on the alternating least squares formulation of CCA, we propose a globally convergent stochastic algorithm, which solves the resulting least squares problems approximately to sufficient accuracy with state-of-the-art stochastic gradient methods for convex optimization. We provide the overall time complexity of our algorithm which significantly improves upon that of previous work. Experimental results demonstrate the superior performance of our algorithm.

Includes members:
  Member "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational optimization on lie groups with examples of leading generalized eigenvalue problems")
  Member "principal component projection without principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "over parameterized adversarial training an analysis overcoming the curse of dimensionality")
  Member "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "large scale approximate kernel canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of multi view representation learning")
  Member "scale up nonlinear component analysis with doubly stochastic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "batch stationary distribution estimation")
  Member "stochastic optimization for deep cca via nonlinear orthogonal iterations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey of multi view representation learning")
  Member "a stochastic pca and svd algorithm with an exponential convergence rate":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning a single neuron with gradient methods")

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)