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: "dropping convexity for more efficient and scalable online multiview learning"
  Abstract: Multiview representation learning is very popular for latent factor analysis. It naturally arises in many data analysis, machine learning, and information retrieval applications to model dependent structures among multiple data sources. For computational convenience, existing approaches usually formulate the multiview representation learning as convex optimization problems, where global optima can be obtained by certain algorithms in polynomial time. However, many pieces of evidence have corroborated that heuristic nonconvex approaches also have good empirical computational performance and convergence to the global optima, although there is a lack of theoretical justification. Such a gap between theory and practice motivates us to study a nonconvex formulation for multiview representation learning, which can be efficiently solved by a simple stochastic gradient descent (SGD) algorithm. We first illustrate the geometry of the nonconvex formulation; Then, we establish asymptotic global rates of convergence to the global optima by diffusion approximations. Numerical experiments are provided to support our theory.

Connected through hyperedge HE-1 (induced by source paper "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"):
  with members:
    - "exploring generalization in deep learning"
    - "deep hyperspherical learning"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "spurious local minima are common in two layer relu neural networks"
    - "online multiview representation learning dropping convexity for better efficiency"
    - "matrix completion has no spurious local minimum"
    - "identity matters in deep learning"
    - "a geometric analysis of phase retrieval"

Connected through hyperedge HE-2 (induced by source paper "towards understanding acceleration tradeoff between momentum and asynchrony in nonconvex stochastic optimization"):
  with members:
    - "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"
    - "deep hyperspherical learning"
    - "revisiting distributed synchronous sgd"
    - "online multiview representation learning dropping convexity for better efficiency"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "on landscape of lagrangian functions and stochastic search for constrained nonconvex optimization"):
  with members:
    - "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"
    - "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-4 (induced by source paper "stochastic canonical correlation analysis"):
  with members:
    - "stochastic approximation for canonical correlation analysis"
    - "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)