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 "greedy and local ratio algorithms in the mapreduce model"):
  Abstract: MapReduce has become the de facto standard model for designing distributed algorithms to process big data on a cluster. There has been considerable research on designing efficient MapReduce algorithms for clustering, graph optimization, and submodular optimization problems. We develop new techniques for designing greedy and local ratio algorithms in this setting. Our randomized local ratio technique gives $2$-approximations for weighted vertex cover and weighted matching, and an $f$-approximation for weighted set cover, all in a constant number of MapReduce rounds. Our randomized greedy technique gives algorithms for maximal independent set, maximal clique, and a $(1+\epsilon)\ln \Delta$-approximation for weighted set cover. We also give greedy algorithms for vertex colouring with $(1+o(1))\Delta$ colours and edge colouring with $(1+o(1))\Delta$ colours.

Includes members:
  Member "coresets meet edcs algorithms for matching and vertex cover on massive graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "round compression for parallel matching algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "a 2 ϵ approximation for maximum weight matching in the semi streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "space optimal semi streaming for 2 e approximate matching":
    also connected through other hyperedges:
      (none)
  Member "streaming weighted matchings optimal meets greedy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a 2 ϵ approximation for maximum weight matching in the semi streaming model")
  Member "randomized composable core sets for distributed submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularized submodular maximization at scale")
  Member "a new framework for distributed submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularized submodular maximization at scale")
  Member "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")

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)