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: "adaptive canonical correlation analysis based on matrix manifolds"
  Abstract: In this paper, we formulate the Canonical Correlation Analysis (CCA) problem on matrix manifolds. This framework provides a natural way for dealing with matrix constraints and tools for building efficient algorithms even in an adaptive setting. Finally, an adaptive CCA algorithm is proposed and applied to a change detection problem in EEG signals.

Connected through hyperedge HE-1 (induced by source paper "canonical correlation analysis for misaligned satellite image change detection"):
  with members:
    - "multi task self supervised visual learning"
    - "playing for data ground truth from computer games"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "globally convergent stochastic optimization for canonical correlation analysis"):
  with members:
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "principal component projection without principal component analysis"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "the fast convergence of incremental pca"
    - "large scale approximate kernel canonical correlation analysis"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

Connected through hyperedge HE-3 (induced by source paper "efficient globally convergent stochastic optimization for canonical correlation analysis"):
  with members:
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "the fast convergence of incremental pca"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

Connected through hyperedge HE-4 (induced by source paper "large scale approximate kernel canonical correlation analysis"):
  with members:
    - "the fast convergence of incremental pca"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "efficient dimensionality reduction for canonical correlation analysis"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-5 (induced by source paper "stochastic optimization for deep cca via nonlinear orthogonal iterations"):
  with members:
    - "the fast convergence of incremental pca"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "an autoencoder approach to learning bilingual word representations"
    - "distributed optimization of deeply nested 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)