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 "streaming symmetric norms via measure concentration"):
  Abstract: We characterize the streaming space complexity of every symmetric norm $l$ (a norm on $\mathbb{R}^n$ invariant under sign-flips and coordinate-permutations), by relating this space complexity to the measure-concentration characteristics of $l$. Specifically, we provide nearly matching upper and lower bounds on the space complexity of calculating a $(1\pm\epsilon)$-approximation to the norm of the stream, for every $0<\epsilon\leq 1/2$. (The bounds match up to $poly(\epsilon^{-1} \log n)$ factors.) We further extend those bounds to any large approximation ratio $D\geq 1.1$, showing that the decrease in space complexity is proportional to $D^2$, and that this factor the best possible. All of the bounds depend on the median of $l(x)$ when $x$ is drawn uniformly from the $l_2$ unit sphere. The same median governs many phenomena in high-dimensional spaces, such as large-deviation bounds and the critical dimension in Dvoretzky's Theorem. #R##N#The family of symmetric norms contains several well-studied norms, such as all $l_p$~norms, and indeed we provide a new explanation for the disparity in space complexity between $p\le 2$ and $p>2$. In addition, we apply our general results to easily derive bounds for several norms that were not studied before in the streaming model, including the top-$k$ norm and the $k$-support norm, which was recently employed for machine learning tasks. #R##N#Overall, these results make progress on two outstanding problems in the area of sublinear algorithms (Problems 5 and 30 in~\url{this http URL}).

Includes members:
  Member "streaming space complexity of nearly all functions of one variable on frequency vectors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  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 "universal sketches for the frequency negative moments and other decreasing streaming sums":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "almost smooth histograms and sliding window graph algorithms")
  Member "sketching and embedding are equivalent for norms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "randomized smoothing of all shapes and sizes")
  Member "tight lower bound for linear sketches of moments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "high probability frequency moment sketches")
  Member "approximating large frequency moments with pick and drop sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "heavy hitters via cluster preserving clustering")
  Member "sketching and streaming entropy via approximation theory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards optimal moment estimation in streaming and distributed models")

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)