human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "globally convergent stochastic optimization for canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of multi view representation learning")
  Member "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "smooth sensitivity based approach for differentially private principal component analysis")
  Member "randomized block krylov methods for stronger and faster approximate singular value decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank")
  Member "large scale approximate kernel canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of multi view representation learning")
  Member "nonparametric canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on universal features for high dimensional learning and inference")
  Member "stochastic optimization for deep cca via nonlinear orthogonal iterations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of multi view representation learning")
  Member "discriminative features via generalized eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "maximum entropy linear manifold for learning discriminative low dimensional representation")
  Member "efficient dimensionality reduction for canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey of multi view representation learning")

Question: Please tell me which class the source paper 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)