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 "optimal streaming and tracking distinct elements with high probability"):
  Abstract: The distinct elements problem is one of the fundamental problems in streaming algorithms --- given a stream of integers in the range $\{1,\ldots,n\}$, we wish to provide a $(1+\varepsilon)$ approximation to the number of distinct elements in the input. After a long line of research optimal solution for this problem with constant probability of success, using $\mathcal{O}(\frac{1}{\varepsilon^2}+\log n)$ bits of space, was given by Kane, Nelson and Woodruff in 2010. #R##N#The standard approach used in order to achieve low failure probability $\delta$, is to take a median of $\log \delta^{-1}$ parallel repetitions of the original algorithm and report the median of computed answers. We show that such a multiplicative space blow-up is unnecessary: we provide an optimal algorithm using $\mathcal{O}(\frac{\log \delta^{-1}}{\varepsilon^2} + \log n)$ bits of space --- matching known lower bounds for this problem. That is, the $\log\delta^{-1}$ factor does not multiply the $\log n$ term. This settles completely the space complexity of the distinct elements problem with respect to all standard parameters. #R##N#We consider also \emph{strong tracking} (or \emph{continuous monitoring}) variant of the distinct elements problem, where we want an algorithm which provides an approximation of the number of distinct elements seen so far, at all times of the stream. We show that this variant can be solved using $\mathcal{O}(\frac{\log \log n + \log \delta^{-1}}{\varepsilon^2} + \log n)$ bits of space, which we show to be optimal.

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 "explicit resilient functions matching ajtai linial":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non malleable extractors and non malleable codes partially optimal constructions")
  Member "tracking the frequency moments at all times":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tracking the ell_2 norm with constant update time")
  Member "a multi round communication lower bound for gap hamming and some consequences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed data summarization in well connected networks")

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)