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 algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
  Abstract: This paper considers the problem of canonical-correlation analysis (CCA) (Hotelling, 1936) and, more broadly, the generalized eigenvector problem for a pair of symmetric matrices. These are two fundamental problems in data analysis and scientific computing with numerous applications in machine learning and statistics (Shi and Malik, 2000; Hardoon et al., 2004; Witten et al., 2009). #R##N#We provide simple iterative algorithms, with improved runtimes, for solving these problems that are globally linearly convergent with moderate dependencies on the condition numbers and eigenvalue gaps of the matrices involved. #R##N#We obtain our results by reducing CCA to the top-$k$ generalized eigenvector problem. We solve this problem through a general framework that simply requires black box access to an approximate linear system solver. Instantiating this framework with accelerated gradient descent we obtain a running time of $O(\frac{z k \sqrt{\kappa}}{\rho} \log(1/\epsilon) \log \left(k\kappa/\rho\right))$ where $z$ is the total number of nonzero entries, $\kappa$ is the condition number and $\rho$ is the relative eigenvalue gap of the appropriate matrices. #R##N#Our algorithm is linear in the input size and the number of components $k$ up to a $\log(k)$ factor. This is essential for handling large-scale matrices that appear in practice. To the best of our knowledge this is the first such algorithm with global linear convergence. We hope that our results prompt further research and ultimately improve the practical running time for performing these important data analysis procedures on large data sets.

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"
    - "stochastic approximation for canonical correlation analysis"
    - "an overview of gradient descent optimization algorithms"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (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"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "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-3 (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-4 (induced by source paper "learning image information for ecommerce queries"):
  with members:
    - "stochastic approximation for 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 "structured sumcor multiview canonical correlation analysis for large scale data"):
  with members:
    - "penalty dual decomposition method for nonsmooth nonconvex optimization"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "a comparison of relaxations of multiset cannonical correlation analysis and applications"

Connected through hyperedge HE-6 (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"
    - "stochastic approximation for canonical correlation analysis"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "the global optimization geometry of low rank matrix optimization"
    - "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-7 (induced by source paper "a decomposition algorithm for sparse generalized eigenvalue problem"):
  with members:
    - "efficient and non convex coordinate descent for symmetric nonnegative matrix factorization"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "incremental training of a detector using online sparse eigendecomposition"
    - "efficiently learning a detection cascade with sparse eigenvectors"
    - "optimal solutions for sparse principal component analysis"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-8 (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"
    - "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)