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 "high probability frequency moment sketches"):
  Abstract: We consider the problem of sketching the $p$-th frequency moment of a vector, $p>2$, with multiplicative error at most $1\pm \epsilon$ and \emph{with high confidence} $1-\delta$. Despite the long sequence of work on this problem, tight bounds on this quantity are only known for constant $\delta$. While one can obtain an upper bound with error probability $\delta$ by repeating a sketching algorithm with constant error probability $O(\log(1/\delta))$ times in parallel, and taking the median of the outputs, we show this is a suboptimal algorithm! Namely, we show optimal upper and lower bounds of $\Theta(n^{1-2/p} \log(1/\delta) + n^{1-2/p} \log^{2/p} (1/\delta) \log n)$ on the sketching dimension, for any constant approximation. Our result should be contrasted with results for estimating frequency moments for $1 \leq p \leq 2$, for which we show the optimal algorithm for general $\delta$ is obtained by repeating the optimal algorithm for constant error probability $O(\log(1/\delta))$ times and taking the median output. We also obtain a matching lower bound for this problem, up to constant factors.

Includes members:
  Member "maximum matching in turnstile streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "taylor polynomial estimator for estimating frequency moments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed data summarization in well connected networks")
  Member "tight lower bound for linear sketches of moments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tight bounds for linear sketches of approximate matchings")
  Member "how robust are linear sketches to adaptive inputs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "span recovery for deep neural networks with applications to input obfuscation")
  Member "fast moment estimation in data streams in optimal space":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "solving tall dense linear programs in nearly linear time")

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)