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 "almost smooth histograms and sliding window graph algorithms"):
  Abstract: We study algorithms for the sliding-window model, an important variant of the data-stream model, in which the goal is to compute some function of a fixed-length suffix of the stream. We explore the smooth histogram framework of Braverman and Ostrovsky (FOCS 2007) for reducing the sliding-window model to the insertion-only streaming model, and extend it to the family of subadditive functions. Specifically, we show that if a subadditive function can be approximated in the ordinary (insertion-only) streaming model, then it could be approximated also in the sliding-window model with approximation ratio larger by factor $2+\epsilon$ and space complexity larger by factor $O(\epsilon^{-1}\log w)$, where $w$ is the window size. #R##N#We then consider graph streams and show that many graph problems are subadditive, including maximum matching and minimum vertex-cover, thereby deriving sliding-window algorithms for them almost for free (using known insertion-only algorithms). One concrete example is a $\mathrm{polylog}(n)$-space algorithm for estimating maximum-matching size in bounded-arboricity graphs with $n$ vertices, whose approximation ratio differs by a factor of $2$ from the insertion-only algorithm of McGregor and Vorotnikova (SODA 2018). #R##N#We also use the same framework to improve the analysis of a known sliding-window algorithm for approximating minimum vertex-cover in general graphs. We improve the approximation ratio $8+\epsilon$ of van Handel (MSc Thesis 2016) to $4+\epsilon$ using the same space complexity $\tilde{O}_\epsilon(n)$.

Includes members:
  Member "the sparse awakens streaming algorithms for matching size estimation in sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "sublinear estimation of weighted matchings in dynamic data streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  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 "universal streaming of subset norms")

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)