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 "continuous monitoring of ell_p norms in data streams"):
  Abstract: In insertion-only streaming, one sees a sequence of indices $a_1, a_2, \ldots, a_m\in [n]$. The stream defines a sequence of $m$ frequency vectors $x^{(1)},\ldots,x^{(m)}\in\mathbb{R}^n$ with $(x^{(t)})_i = |\{j : j\in[t], a_j = i\}|$. That is, $x^{(t)}$ is the frequency vector after seeing the first $t$ items in the stream. Much work in the streaming literature focuses on estimating some function $f(x^{(m)})$. Many applications though require obtaining estimates at time $t$ of $f(x^{(t)})$, for every $t\in[m]$. Naively this guarantee is obtained by devising an algorithm with failure probability $\ll 1/m$, then performing a union bound over all stream updates to guarantee that all $m$ estimates are simultaneously accurate with good probability. When $f(x)$ is some $\ell_p$ norm of $x$, recent works have shown that this union bound is wasteful and better space complexity is possible for the continuous monitoring problem, with the strongest known results being for $p=2$ [HTY14, BCIW16, BCINWW17]. In this work, we improve the state of the art for all $0<p<2$, which we obtain via a novel analysis of Indyk's $p$-stable sketch [Indyk06].

Includes members:
  Member "bptree an ell_2 heavy hitters algorithm using constant memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "beating countsketch for heavy hitters in insertion streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rambo repeated and merged bloom filter for multiple set membership testing msmt in sub linear time")
  Member "streaming symmetric norms via measure concentration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "taylor polynomial estimator for estimating frequency moments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed data summarization in well connected networks")
  Member "universal sketches for the frequency negative moments and other decreasing streaming sums":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "almost smooth histograms and sliding window graph algorithms")
  Member "tracking the frequency moments at all times":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tracking the ell_2 norm with constant update time")
  Member "fast moment estimation in data streams in optimal space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "measuring independence of datasets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding subcube heavy hitters in analytics data streams")

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)