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: "space optimal semi streaming for 2 e approximate matching"
  Abstract: In a recent breakthrough, Paz and Schwartzman [SODA'17] presented a single-pass ($2+\epsilon$)-approximation algorithm for the maximum weight matching problem in the semi-streaming model. Their algorithm uses $O(n\log^2 n)$ bits of space, for any constant $\epsilon>0$. #R##N#In this note, we present a different analysis, for essentially the same algorithm, that improves the space complexity to the optimal bound of $O(n\log n)$ bits, while also providing a more intuitive explanation of the process.

Connected through hyperedge HE-1 (induced by source paper "greedy and local ratio algorithms in the mapreduce model"):
  with members:
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "round compression for parallel matching algorithms"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "streaming weighted matchings optimal meets greedy"
    - "randomized composable core sets for distributed submodular maximization"
    - "a new framework for distributed submodular maximization"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "distributed k means and k median clustering on general topologies"

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)