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: "efficient 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 gradient based optimization algorithms have been recently proposed to solve this problem, no global convergence guarantee was provided by any of them. Inspired by the alternating least squares/power iterations formulation of CCA, and the shift-and-invert preconditioning method for PCA, we propose two globally convergent meta-algorithms for CCA, both of which transform the original problem into sequences of least squares problems that need only be solved approximately. We instantiate the meta-algorithms with state-of-the-art SGD methods and obtain time complexities that significantly improve upon that of previous work. Experimental results demonstrate their superior performance.

Connected through hyperedge HE-1 (induced by source paper "a general scoring rule for randomized kernel approximation with application to canonical correlation analysis"):
  with members:
    - "on sampling random features from empirical leverage scores implementation and theoretical guarantees"
    - "but how does it work in theory linear svm with random features"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "data driven random fourier features using stein effect"
    - "stochastic approximation for canonical correlation analysis"
    - "orthogonal random features"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "large scale approximate kernel canonical correlation analysis"

Connected through hyperedge HE-2 (induced by source paper "neural network assisted nonlinear multiview component analysis identifiability and algorithm"):
  with members:
    - "structured sumcor multiview canonical correlation analysis for large scale data"
    - "learning nonlinear mixtures identifiability and algorithm"
    - "on identifiability of nonnegative matrix factorization"
    - "deep generalized canonical correlation analysis"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "robust matrix elastic net based canonical correlation analysis an effective algorithm for multi view unsupervised learning"):
  with members:
    - "constrained low rank learning using least squares based regularization"
    - "unsupervised learning of long term motion dynamics for videos"
    - "a comprehensive survey on cross modal retrieval"
    - "large scale approximate kernel canonical correlation analysis"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "a survey on multi view learning"
    - "trace lasso a trace norm regularization for correlated designs"

Connected through hyperedge HE-4 (induced by source paper "stochastic approximation for canonical correlation analysis"):
  with members:
    - "stochastic canonical correlation analysis"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

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)