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: "universal sketches for the frequency negative moments and other decreasing streaming sums"
  Abstract: Given a stream with frequencies $f_d$, for $d\in[n]$, we characterize the space necessary for approximating the frequency negative moments $F_p=\sum |f_d|^p$, where $p<0$ and the sum is taken over all items $d\in[n]$ with nonzero frequency, in terms of $n$, $\epsilon$, and $m=\sum |f_d|$. To accomplish this, we actually prove a much more general result. Given any nonnegative and nonincreasing function $g$, we characterize the space necessary for any streaming algorithm that outputs a $(1\pm\epsilon)$-approximation to $\sum g(|f_d|)$, where again the sum is over items with nonzero frequency. The storage required is expressed in the form of the solution to a relatively simple nonlinear optimization problem, and the algorithm is universal for $(1\pm\epsilon)$-approximations to any such sum where the applied function is nonnegative, nonincreasing, and has the same or smaller space complexity as $g$. This partially answers an open question of Nelson (IITK Workshop Kanpur, 2009).

Connected through hyperedge HE-1 (induced by source paper "almost smooth histograms and sliding window graph algorithms"):
  with members:
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "sublinear estimation of weighted matchings in dynamic data streams"

Connected through hyperedge HE-2 (induced by source paper "universal streaming of subset norms"):
  with members:
    - "revisiting frequency moment estimation in random order streams"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "streaming symmetric norms via measure concentration"
    - "on sketching quadratic forms"
    - "fast moment estimation in data streams in optimal space"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "streaming algorithms from precision sampling"

Connected through hyperedge HE-3 (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"
    - "taylor polynomial estimator for estimating frequency moments"
    - "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-4 (induced by source paper "independent set size approximation in graph streams"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "sublinear time estimation of degree distribution moments the arboricity connection"
    - "space constrained interval selection"

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"
    - "taylor polynomial estimator for estimating frequency moments"
    - "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"
    - "taylor polynomial estimator for estimating frequency moments"
    - "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 space complexity of nearly all functions of one variable on frequency vectors"):
  with members:
    - "sketching and embedding are equivalent for norms"
    - "automatic optimization for mapreduce programs"

Connected through hyperedge HE-8 (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"
    - "taylor polynomial estimator for estimating frequency moments"
    - "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)