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: "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
  Abstract: We provide faster algorithms and improved sample complexities for approximating the top eigenvector of a matrix. #R##N#Offline Setting: Given an $n \times d$ matrix $A$, we show how to compute an $\epsilon$ approximate top eigenvector in time $\tilde O ( [nnz(A) + \frac{d \cdot sr(A)}{gap^2}]\cdot \log 1/\epsilon )$ and $\tilde O([\frac{nnz(A)^{3/4} (d \cdot sr(A))^{1/4}}{\sqrt{gap}}]\cdot \log1/\epsilon )$. Here $sr(A)$ is the stable rank and $gap$ is the multiplicative eigenvalue gap. By separating the $gap$ dependence from $nnz(A)$ we improve on the classic power and Lanczos methods. We also improve prior work using fast subspace embeddings and stochastic optimization, giving significantly improved dependencies on $sr(A)$ and $\epsilon$. Our second running time improves this further when $nnz(A) \le \frac{d\cdot sr(A)}{gap^2}$. #R##N#Online Setting: Given a distribution $D$ with covariance matrix $\Sigma$ and a vector $x_0$ which is an $O(gap)$ approximate top eigenvector for $\Sigma$, we show how to refine to an $\epsilon$ approximation using $\tilde O(\frac{v(D)}{gap^2} + \frac{v(D)}{gap \cdot \epsilon})$ samples from $D$. Here $v(D)$ is a natural variance measure. Combining our algorithm with previous work to initialize $x_0$, we obtain a number of improved sample complexity and runtime results. For general distributions, we achieve asymptotically optimal accuracy as a function of sample size as the number of samples grows large. #R##N#Our results center around a robust analysis of the classic method of shift-and-invert preconditioning to reduce eigenvector computation to approximately solving a sequence of linear systems. We then apply fast SVRG based approximate system solvers to achieve our claims. We believe our results suggest the general effectiveness of shift-and-invert based approaches and imply that further computational gains may be reaped in practice.

Connected through hyperedge HE-1 (induced by source paper "smooth sensitivity based approach for differentially private principal component analysis"):
  with members:
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast rates for empirical risk minimization of strict saddle problems"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "efficient frequent directions algorithm for sparse matrices"
    - "convergence of stochastic gradient descent for pca"
    - "low rank approximation and regression in input sparsity time"
    - "beating randomized response on incoherent matrices"
    - "beyond worst case analysis in private singular vector computation"

Connected through hyperedge HE-2 (induced by source paper "faster eigenvector computation via shift and invert preconditioning"):
  with members:
    - "convergence of stochastic gradient descent for pca"
    - "the fast convergence of incremental pca"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "sdca without duality"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"):
  with members:
    - "geometric median in nearly linear time"
    - "convergence of stochastic gradient descent for pca"
    - "the fast convergence of incremental pca"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal approximate matrix product in terms of stable rank"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "the noisy power method a meta algorithm with applications"

Connected through hyperedge HE-4 (induced by source paper "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"):
  with members:
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "large scale approximate kernel canonical correlation analysis"
    - "nonparametric canonical correlation analysis"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "discriminative features via generalized eigenvectors"
    - "efficient dimensionality reduction for canonical correlation analysis"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-5 (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"
    - "the fast convergence of incremental pca"
    - "large scale approximate kernel canonical correlation analysis"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "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-6 (induced by source paper "sdca without duality regularization and individual convexity"):
  with members:
    - "univr a universal variance reduction framework for proximal stochastic gradient method"
    - "new optimisation methods for machine learning"
    - "sdca without duality"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-7 (induced by source paper "faster low rank approximation using adaptive gap based preconditioning"):
  with members:
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "convergence of stochastic gradient descent for pca"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "improved analyses of the randomized power method and block lanczos method"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-8 (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"
    - "the fast convergence of incremental pca"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "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"

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.DS(Data Structures and Algorithms)