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: "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 $\epsilon = \Omega(1)$.

Connected through hyperedge HE-1 (induced by source paper "distributed data summarization in well connected networks"):
  with members:
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "optimal gossip algorithms for exact and approximate quantile computations"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "a multi round communication lower bound for gap hamming and some consequences"

Connected through hyperedge HE-2 (induced by source paper "high probability frequency moment sketches"):
  with members:
    - "maximum matching in turnstile streams"
    - "tight lower bound for linear sketches of moments"
    - "how robust are linear sketches to adaptive inputs"
    - "fast moment estimation in data streams in optimal space"

Connected through hyperedge HE-3 (induced by source paper "approximating approximate pattern matching"):
  with members:
    - "the k mismatch problem revisited"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

Connected through hyperedge HE-4 (induced by source paper "continuous monitoring of ell_p norms in data streams"):
  with members:
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "beating countsketch for heavy hitters in insertion streams"
    - "streaming symmetric norms via measure concentration"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "tracking the frequency moments at all times"
    - "fast moment estimation in data streams in optimal space"
    - "measuring independence of datasets"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-5 (induced by source paper "heavy hitters via cluster preserving clustering"):
  with members:
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "beating countsketch for heavy hitters in insertion streams"
    - "linear time list recovery of high rate expander codes"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "partitioning well clustered graphs spectral clustering works"
    - "constant factor approximation for balanced cut in the pie model"
    - "flow based algorithms for local graph clustering"
    - "for all sparse recovery in near optimal time"

Connected through hyperedge HE-6 (induced by source paper "on approximating functions of the singular values in a stream"):
  with members:
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "spectrum estimation from samples"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "sketching as a tool for numerical linear algebra"
    - "sketching and embedding are equivalent for norms"
    - "streaming sums in sublinear space"
    - "optimal error of query sets under the differentially private matrix mechanism"

Connected through hyperedge HE-7 (induced by source paper "streaming symmetric norms via measure concentration"):
  with members:
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "sketching and embedding are equivalent for norms"
    - "tight lower bound for linear sketches of moments"
    - "approximating large frequency moments with pick and drop sampling"
    - "sketching and streaming entropy via approximation theory"

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)