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: "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.

Connected through hyperedge HE-1 (induced by source paper "a survey of multi view representation learning"):
  with members:
    - "word2visualvec cross media retrieval by visual feature prediction"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "deep cross modal hashing"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-2 (induced by source paper "structured sumcor multiview canonical correlation analysis for large scale data"):
  with members:
    - "penalty dual decomposition method for nonsmooth nonconvex optimization"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "a comparison of relaxations of multiset cannonical correlation analysis and applications"

Connected through hyperedge HE-3 (induced by source paper "stochastic canonical correlation analysis"):
  with members:
    - "dropping convexity for more efficient and scalable online multiview learning"
    - "stochastic approximation for canonical correlation analysis"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "large scale approximate kernel canonical correlation analysis"

Connected through hyperedge HE-4 (induced by source paper "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"):
  with members:
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "large scale approximate kernel canonical correlation analysis"
    - "nonparametric canonical correlation analysis"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "discriminative features via generalized eigenvectors"
    - "efficient dimensionality reduction for canonical correlation analysis"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

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)