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 "nearly optimal distinct elements and heavy hitters on sliding windows"):
  Abstract: We study the distinct elements and $\ell_p$-heavy hitters problems in the sliding window model, where only the most recent $n$ elements in the data stream form the underlying set. We first introduce the composable histogram, a simple twist on the exponential (Datar et al., SODA 2002) and smooth histograms (Braverman and Ostrovsky, FOCS 2007) that may be of independent interest. We then show that the composable histogram along with a careful combination of existing techniques to track either the identity or frequency of a few specific items suffices to obtain algorithms for both distinct elements and $\ell_p$-heavy hitters that are nearly optimal in both $n$ and $\epsilon$. #R##N#Applying our new composable histogram framework, we provide an algorithm that outputs a $(1+\epsilon)$-approximation to the number of distinct elements in the sliding window model and uses $\O{\frac{1}{\epsilon^2}\log n\log\frac{1}{\epsilon}\log\log n+\frac{1}{\epsilon}\log^2 n}$ bits of space. For $\ell_p$-heavy hitters, we provide an algorithm using space $\mathcal{O}\left(\frac{1}{\epsilon^p}\log^2 n\left(\log\log n+\log\frac{1}{\epsilon}\right)\right)$ for $0<p\le 2$, improving upon the best-known algorithm for $\ell_2$-heavy hitters (Braverman et al., COCOON 2014), which has space complexity $\mathcal{O}\left(\frac{1}{\epsilon^4}\log^3 n\right)$. We also show complementing nearly optimal lower bounds of $\Omega\left(\frac{1}{\epsilon}\log^2 n+\frac{1}{\epsilon^2}\log n\right)$ for distinct elements and $\Omega\left(\frac{1}{\epsilon^p}\log^2 n\right)$ for $\ell_p$-heavy hitters, both tight up to $\mathcal{O}(\log\log n)$ and $\mathcal{O}\left(\log\frac{1}{\epsilon}\right)$ factors.

Includes members:
  Member "optimal streaming and tracking distinct elements with high probability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast hashing with strong concentration bounds")
  Member "continuous monitoring of ell_p norms in data streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "submodular optimization over sliding windows":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bring your own greedy max near optimal 1 2 approximations for submodular knapsack")
  Member "bptree an ell_2 heavy hitters algorithm using constant memory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "submodular maximization over sliding windows":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tight bounds on the round complexity of the distributed maximum coverage problem")
  Member "heavy hitters via cluster preserving clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "beating countsketch for heavy hitters in insertion streams":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rambo repeated and merged bloom filter for multiple set membership testing msmt in sub linear time")
  Member "how to catch l 2 heavy hitters on sliding windows":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "l p pattern matching in a stream")

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)