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 "stochastic canonical correlation analysis"):
  Abstract: We study the sample complexity of canonical correlation analysis (CCA), \ie, the number of samples needed to estimate the population canonical correlation and directions up to arbitrarily small error. With mild assumptions on the data distribution, we show that in order to achieve $\epsilon$-suboptimality in a properly defined measure of alignment between the estimated canonical directions and the population solution, we can solve the empirical objective exactly with $N(\epsilon, \Delta, \gamma)$ samples, where $\Delta$ is the singular value gap of the whitened cross-covariance matrix and $1/\gamma$ is an upper bound of the condition number of auto-covariance matrices. Moreover, we can achieve the same learning accuracy by drawing the same level of samples and solving the empirical objective approximately with a stochastic optimization algorithm; this algorithm is based on the shift-and-invert power iterations and only needs to process the dataset for $\mathcal{O}\left(\log \frac{1}{\epsilon} \right)$ passes. Finally, we show that, given an estimate of the canonical correlation, the streaming version of the shift-and-invert power iterations achieves the same learning accuracy with the same level of sample complexity, by processing the data only once.

Includes members:
  Member "dropping convexity for more efficient and scalable online multiview learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations")
  Member "stochastic approximation for canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variational optimization on lie groups with examples of leading generalized eigenvalue problems")
  Member "lazysvd even faster svd decomposition yet without agonizing pain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational optimization on lie groups with examples of leading generalized eigenvalue problems")
  Member "globally convergent stochastic optimization for canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of multi view representation learning")
  Member "faster eigenvector computation via shift and invert preconditioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "principal component projection and regression in nearly linear time through asymmetric svrg")
  Member "large scale approximate kernel canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)