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: "efficient frequent directions algorithm for sparse matrices"
  Abstract: This paper describes Sparse Frequent Directions, a variant of Frequent Directions for sketching sparse matrices. It resembles the original algorithm in many ways: both receive the rows of an input matrix $A^{n \times d}$ one by one in the streaming setting and compute a small sketch $B \in R^{\ell \times d}$. Both share the same strong (provably optimal) asymptotic guarantees with respect to the space-accuracy tradeoff in the streaming setting. However, unlike Frequent Directions which runs in $O(nd\ell)$ time regardless of the sparsity of the input matrix $A$, Sparse Frequent Directions runs in $\tilde{O} (nnz(A)\ell + n\ell^2)$ time. Our analysis loosens the dependence on computing the Singular Value Decomposition (SVD) as a black box within the Frequent Directions algorithm. Our bounds require recent results on the properties of fast approximate SVD computations. Finally, we empirically demonstrate that these asymptotic improvements are practical and significant on real and synthetic data.

Connected through hyperedge HE-1 (induced by source paper "a deterministic streaming sketch for ridge regression"):
  with members:
    - "robust frequent directions with application in online learning"
    - "online row sampling"
    - "improved practical matrix sketching with guarantees"
    - "frequent directions simple and deterministic matrix sketching"
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-2 (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"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "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-3 (induced by source paper "graph summarization methods and applications a survey"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "by chance is not enough preserving relative density through non uniform sampling"
    - "node2vec scalable feature learning for networks"
    - "compressing graphs and indexes with recursive graph bisection"
    - "network structure inference a survey motivations methods and applications"
    - "pnp fast path ensemble method for movie design"
    - "an empirical comparison of the summarization power of graph clustering methods"
    - "line large scale information network embedding"

Connected through hyperedge HE-4 (induced by source paper "efficient second order online learning by sketching"):
  with members:
    - "solving ridge regression using sketched preconditioned svrg"
    - "the fast convergence of incremental pca"
    - "scale free algorithms for online linear optimization"
    - "frequent directions simple and deterministic matrix sketching"
    - "faster sgd using sketched conditioning"
    - "sketching as a tool for numerical linear algebra"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "one pass auc optimization"

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)