human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "improved practical matrix sketching with guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deterministic streaming sketch for ridge regression")
  Member "randomized block krylov methods for stronger and faster approximate singular value decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank")
  Member "frequent directions simple and deterministic matrix sketching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deterministic streaming sketch for ridge regression")
  Member "optimal cur matrix decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "relative errors for deterministic low rank matrix approximations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees")
  Member "randomized algorithms for low rank matrix factorizations sharp performance bounds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "randomized spectral clustering in large scale stochastic block models")
  Member "low rank approximation and regression in input sparsity time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "simple and deterministic matrix sketching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")

Question: Please tell me which class the source paper 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)