human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "distributed coverage maximization via sketching"
  Abstract: Coverage problems are central in optimization and have a wide range of applications in data mining and machine learning. While several distributed algorithms have been developed for coverage problems, the existing methods suffer from several limitations, e.g., they all achieve either suboptimal approximation guarantees or suboptimal space and memory complexities. In addition, previous algorithms developed for submodular maximization assume oracle access, and ignore the computational complexity of communicating large subsets or computing the size of the union of the subsets in this subfamily. In this paper, we develop an improved distributed algorithm for the $k$-cover and the set cover with $\lambda$ outliers problems, with almost optimal approximation guarantees, almost optimal memory complexity, and linear communication complexity running in only four rounds of computation. Finally, we perform an extensive empirical study of our algorithms on a number of publicly available real data sets, and show that using sketches of size $30$ to $600$ times smaller than the input, one can solve the coverage maximization problem with quality very close to that of the state-of-the-art single-machine algorithm.

Connected through hyperedge HE-1 (induced by source paper "tight bounds on the round complexity of the distributed maximum coverage problem"):
  with members:
    - "distributed partial clustering"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "on estimating maximum matching size in graph streams"
    - "combinatorial auctions do need modest interaction"
    - "submodular optimization over sliding windows"
    - "almost optimal streaming algorithms for coverage problems"
    - "better streaming algorithms for the maximum coverage problem"
    - "submodular maximization over sliding windows"

Connected through hyperedge HE-2 (induced by source paper "metric sublinear algorithms via linear sampling"):
  with members:
    - "sublinear algorithms for maxcut and correlation clustering"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "almost optimal streaming algorithms for coverage problems"
    - "towards tight bounds for the streaming set cover problem"
    - "densest subgraph in dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "almost optimal streaming algorithms for coverage problems"):
  with members:
    - "submodular optimization under noise"
    - "better streaming algorithms for the maximum coverage problem"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"
    - "randomized composable core sets for distributed submodular maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"

Question: Please tell me which class the center node 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)