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: "even faster svd decomposition yet without agonizing pain"
  Abstract: We study k-SVD that is to obtain the first k singular vectors of a matrix $A$ approximately. Recently, a few breakthroughs have been discovered on k-SVD: Musco and Musco [1] provided the first gap-free theorem for the block Krylov method, Shamir [2] discovered the first variance-reduction stochastic method, and Bhojanapalli et al. [3] provided the fastest $O(\mathsf{nnz}(A) + \mathsf{poly}(1/\varepsilon))$-type of algorithm using alternating minimization. #R##N#In this paper, put forward a new framework for SVD and improve the above breakthroughs. We obtain faster gap-free convergence rate outperforming [1], we obtain the first accelerated AND stochastic method outperforming [2]. In the $O(\mathsf{nnz}(A) + \mathsf{poly}(1/\varepsilon))$ running-time regime, we outperform [3] in certain parameter regimes without even using alternating minimization.

Connected through hyperedge HE-1 (induced by source paper "meta learning for mixed linear regression"):
  with members:
    - "learning mixtures of linear regressions in subexponential time via fourier moments"
    - "sublinear optimal policy value estimation in contextual bandits"
    - "meta learning with implicit gradients"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "estimating learnability in the sublinear data regime"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-2 (induced by source paper "communication efficient algorithms for distributed stochastic principal component analysis"):
  with members:
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "convergence of stochastic gradient descent for pca"
    - "the fast convergence of incremental pca"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "optimal principal component analysis in distributed and streaming models"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "communication efficient distributed dual coordinate ascent"
    - "improved distributed principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "follow the compressed leader faster online learning of eigenvectors and faster mmwu"):
  with members:
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "sdca without duality regularization and individual convexity"
    - "pca with gaussian perturbations"
    - "convergence of stochastic gradient descent for pca"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "spectral smoothing via random matrix perturbations"
    - "online linear optimization via smoothing"
    - "the noisy power method a meta algorithm with applications"

Connected through hyperedge HE-4 (induced by source paper "improved svrg for non strongly convex or sum of non convex objectives"):
  with members:
    - "introduction to online convex optimization"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "stop wasting my gradients practical svrg"
    - "sdca without duality"
    - "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"

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.NA(Numerical Analysis)