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 "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"):
  Abstract: Estimating the size of the maximum matching is a canonical problem in graph algorithms, and one that has attracted extensive study over a range of different computational models. We present improved streaming algorithms for approximating the size of maximum matching with sparse (bounded arboricity) graphs. #R##N#* Insert-Only Streams: We present a one-pass algorithm that takes O(c log^2 n) space and approximates the size of the maximum matching in graphs with arboricity c within a factor of O(c). This improves significantly on the state-of-the-art O~(cn^{2/3})-space streaming algorithms. #R##N#* Dynamic Streams: Given a dynamic graph stream (i.e., inserts and deletes) of edges of an underlying c-bounded arboricity graph, we present a one-pass algorithm that uses space O~(c^{10/3}n^{2/3}) and returns an O(c)-estimator for the size of the maximum matching. This algorithm improves the state-of-the-art O~(cn^{4/5})-space algorithms, where the O~(.) notation hides logarithmic in $n$ dependencies. #R##N#* Random Order Streams: For randomly ordered graph streams, we present a two-pass streaming algorithm that outputs a (1+eps)-approximation of the maximal matching size using O(d^{d^{d+6}}) space for graphs with maximum degree bounded by d. The previous best algorithm for this setting is an O(\log d) approximation. Our algorithm is the first with an approximation guarantee independent of d. Our result is obtained by simulating a property-testing result in a streaming setting, a technique which might be of independent interest. #R##N#In contrast to the previous works, our results take more advantage of the streaming access to the input and characterize the matching size based on the ordering of the edges in the stream in addition to the degree distributions and structural properties of the sparse graphs.

Includes members:
  Member "sublinear estimation of weighted matchings in dynamic data streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "better bounds for matchings in the streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "maximum matching in semi streaming with few passes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "improved approximation guarantees for weighted matching in the semi streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "weighted matching in the semi streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")

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)