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 "taylor polynomial estimator for estimating frequency moments"):
  Abstract: We present a randomized algorithm for estimating the $p$th moment $F_p$ of the frequency vector of a data stream in the general update (turnstile) model to within a multiplicative factor of $1 \pm \epsilon$, for $p > 2$, with high constant confidence. For $0 < \epsilon \le 1$, the algorithm uses space $O( n^{1-2/p} \epsilon^{-2} + n^{1-2/p} \epsilon^{-4/p} \log (n))$ words. This improves over the current bound of $O(n^{1-2/p} \epsilon^{-2-4/p} \log (n))$ words by Andoni et. al. in \cite{ako:arxiv10}. Our space upper bound matches the lower bound of Li and Woodruff \cite{liwood:random13} for $\epsilon = (\log (n))^{-\Omega(1)}$ and the lower bound of Andoni et. al. \cite{anpw:icalp13} for $\epsilon = \Omega(1)$.

Includes members:
  Member "approximating large frequency moments with o n 1 2 k bits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "streaming sums in sublinear space")
  Member "tight lower bound for linear sketches of moments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "high probability frequency moment sketches")
  Member "a lower bound for estimating high moments of a data stream":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on approximating functions of the singular values in a stream")
  Member "tight bounds for distributed functional monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "1 eps approximate sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "lower bounds for sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "querying a matrix through matrix vector products")
  Member "online learning of noisy data with kernels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "marginalizing corrupted features")
  Member "streaming algorithms from precision sampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal sketching for kronecker product regression and low rank approximation")

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)