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: "stochastic approximation for canonical correlation analysis"
  Abstract: We propose novel first-order stochastic approximation algorithms for canonical correlation analysis (CCA). Algorithms presented are instances of inexact matrix stochastic gradient (MSG) and inexact matrix exponentiated gradient (MEG), and achieve $\epsilon$-suboptimality in the population objective in $\operatorname{poly}(\frac{1}{\epsilon})$ iterations. We also consider practical variants of the proposed algorithms and compare them with other methods for CCA both theoretically and empirically.

Connected through hyperedge HE-1 (induced by source paper "variational optimization on lie groups with examples of leading generalized eigenvalue problems"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "stochastic modified equations and dynamics of stochastic gradient algorithms i mathematical foundations"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "an overview of gradient descent optimization algorithms"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (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"
    - "orthogonal random features"
    - "efficient globally convergent stochastic optimization for canonical correlation analysis"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "large scale approximate kernel canonical correlation analysis"

Connected through hyperedge HE-4 (induced by source paper "learning image information for ecommerce queries"):
  with members:
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "generative adversarial text to image synthesis"
    - "deep residual learning for image recognition"
    - "feature hashing for large scale multitask learning"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-5 (induced by source paper "on landscape of lagrangian functions and stochastic search for constrained nonconvex optimization"):
  with members:
    - "dropping convexity for more efficient and scalable online multiview learning"
    - "online multiview representation learning dropping convexity for better efficiency"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "the global optimization geometry of low rank matrix optimization"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"

Connected through hyperedge HE-6 (induced by source paper "non linear canonical correlation analysis a compressed representation approach"):
  with members:
    - "bregman divergence bounds and universality properties of the logarithmic loss"
    - "on the universality of the logistic loss function"
    - "on deep multi view representation learning objectives and optimization"
    - "nonparametric canonical correlation analysis"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-7 (induced by source paper "stochastic canonical correlation analysis"):
  with members:
    - "dropping convexity for more efficient and scalable online multiview learning"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "large scale approximate kernel canonical correlation analysis"

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)