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: "scale up nonlinear component analysis with doubly stochastic gradients"
  Abstract: Nonlinear component analysis such as kernel Principle Component Analysis (KPCA) and kernel Canonical Correlation Analysis (KCCA) are widely used in machine learning, statistics and data analysis, but they can not scale up to big datasets. Recent attempts have employed random feature approximations to convert the problem to the primal form for linear computational complexity. However, to obtain high quality solutions, the number of random features should be the same order of magnitude as the number of data points, making such approach not directly applicable to the regime with millions of data points. #R##N#We propose a simple, computationally efficient, and memory friendly algorithm based on the "doubly stochastic gradients" to scale up a range of kernel nonlinear component analysis, such as kernel PCA, CCA and SVD. Despite the \emph{non-convex} nature of these problems, our method enjoys theoretical guarantees that it converges at the rate $\tilde{O}(1/t)$ to the global optimum, even for the top $k$ eigen subspace. Unlike many alternatives, our algorithm does not require explicit orthogonalization, which is infeasible on big datasets. We demonstrate the effectiveness and scalability of our algorithm on large scale synthetic and real world datasets.

Connected through hyperedge HE-1 (induced by source paper "batch stationary distribution estimation"):
  with members:
    - "adversarial learning of a sampler based on an unnormalized distribution"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "online factorization and partition of complex networks from random walks"
    - "using options and covariance testing for long horizon off policy policy evaluation"
    - "learning from conditional distributions via dual embeddings"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "the laplacian in rl learning representations with efficient approximations"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "data efficient hierarchical reinforcement learning"
    - "spectral inference networks unifying spectral methods with deep learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "eigenoption discovery through the deep successor representation"
    - "curiosity driven exploration by self supervised prediction"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "exploration a study of count based exploration for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "streaming kernel pca with tilde o sqrt n random features"):
  with members:
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "streaming kernel principal component analysis"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-4 (induced by source paper "robust matrix elastic net based canonical correlation analysis an effective algorithm for multi view unsupervised learning"):
  with members:
    - "constrained low rank learning using least squares based regularization"
    - "unsupervised learning of long term motion dynamics for videos"
    - "a comprehensive survey on cross modal retrieval"
    - "efficient globally convergent stochastic optimization for canonical correlation analysis"
    - "large scale approximate kernel canonical correlation analysis"
    - "a survey on multi view learning"
    - "trace lasso a trace norm regularization for correlated designs"

Connected through hyperedge HE-5 (induced by source paper "online factorization and partition of complex networks from random walks"):
  with members:
    - "the spacey random walk a stochastic process for higher order data"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "geometric median in nearly linear time"

Connected through hyperedge HE-6 (induced by source paper "globally convergent stochastic optimization for canonical correlation analysis"):
  with members:
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "principal component projection without principal component analysis"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "the fast convergence of incremental pca"
    - "large scale approximate kernel canonical correlation analysis"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "adaptive canonical correlation analysis based on matrix manifolds"

Connected through hyperedge HE-7 (induced by source paper "efficient globally convergent stochastic optimization for canonical correlation analysis"):
  with members:
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "the fast convergence of incremental pca"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "adaptive canonical correlation analysis based on matrix manifolds"

Connected through hyperedge HE-8 (induced by source paper "recycling randomness with structure for sublinear time kernel expansions"):
  with members:
    - "binary embeddings with structured hashed projections"
    - "fastfood approximate kernel expansions in loglinear time"
    - "randomized algorithms for matrices and data"
    - "restricted isometries for partial random circulant matrices"

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)