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 "universal streaming of subset norms"):
  Abstract: Most known algorithms in the streaming model of computation aim to approximate a single function such as an $\ell_p$-norm. In 2009, Nelson [\url{https://sublinear.info}, Open Problem 30] asked if it possible to design \emph{universal algorithms}, that simultaneously approximate multiple functions of the stream. In this paper we answer the question of Nelson for the class of \emph{subset $\ell_0$-norms} in the insertion-only frequency-vector model. Given a family of subsets $\mathcal{S}\subset 2^{[n]}$, we provide a single streaming algorithm that can $(1\pm \epsilon)$-approximate the subset-norm for every $S\in\mathcal{S}$. Here, the subset-$\ell_p$-norm of $v\in \mathbb{R}^n$ with respect to set $S\subseteq [n]$ is the $\ell_p$-norm of vector $v_{|S}$ (which denotes restricting $v$ to $S$, by zeroing all other coordinates).  Our main result is a near-tight characterization of the space complexity of every family $\mathcal{S}\subset 2^{[n]}$ of subset-$\ell_0$-norms in insertion-only streams, expressed in terms of the "heavy-hitter dimension" of $\mathcal{S}$, a new combinatorial quantity that is related to the VC-dimension of $\mathcal{S}$. In contrast, we show that the more general turnstile and sliding-window models require a much larger space usage. All these results easily extend to $\ell_1$.  In addition, we design algorithms for two other subset-$\ell_p$-norm variants. These can be compared to the Priority Sampling algorithm of Duffield, Lund and Thorup [JACM 2007], which achieves additive approximation $\epsilon\|{v}\|$ for all possible subsets ($\mathcal{S}=2^{[n]}$) in the entry-wise update model. One of our algorithms extends this algorithm to handle turnstile updates, and another one achieves multiplicative approximation given a family $\mathcal{S}$.

Includes members:
  Member "revisiting frequency moment estimation in random order streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "streaming space complexity of nearly all functions of one variable on frequency vectors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "bptree an ell_2 heavy hitters algorithm using constant memory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "streaming symmetric norms via measure concentration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "on sketching quadratic forms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "universal sketches for the frequency negative moments and other decreasing streaming sums":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "almost smooth histograms and sliding window graph algorithms")
  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 "an optimal lower bound on the communication complexity of gap hamming distance":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")

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)