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: "streaming weighted matchings optimal meets greedy"
  Abstract: We consider the problem of approximating a maximum weighted matching, when the edges of an underlying weighted graph $G(V,E)$ are revealed in a streaming fashion. We analyze a variant of the previously best-known $(4+\epsilon)$-approximation algorithm due to Crouch and Stubbs (APPROX, 2014), and prove their conjecture that it achieves a tight approximation factor of $3.5+\epsilon$. #R##N#The algorithm splits the stream into substreams on which it runs a greedy maximum matching algorithm. At the end of the stream, the selected edges are given as input to an optimal maximum weighted matching algorithm. To analyze the approximation guarantee, we develop a novel charging argument in which we decompose the edges of a maximum weighted matching of $G$ into a few natural classes, and then charge them separately to the edges of the matching output by our algorithm.

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"
    - "space optimal semi streaming for 2 e approximate matching"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a 2 ϵ approximation for maximum weight matching in the semi streaming model"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "submodular maximization meets streaming matchings matroids and more"
    - "better bounds for matchings in the streaming model"
    - "improved approximation guarantees for weighted matching in the semi streaming model"
    - "buyback problem approximate matroid intersection with cancellation costs"
    - "weighted matching in the semi streaming model"

Connected through hyperedge HE-3 (induced by source paper "space optimal semi streaming for 2 e approximate matching"):
  with members:
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "improved approximation guarantees for weighted matching in the semi streaming model"

Connected through hyperedge HE-4 (induced by source paper "estimating weighted matchings in o n space"):
  with members:
    - "the shortest augmenting paths for online matchings on trees"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "better bounds for matchings in the streaming model"
    - "simple deterministic algorithms for fully dynamic maximal matching"
    - "maintaining approximate maximum weighted matching in fully dynamic graphs"
    - "maximum matching in semi streaming with few passes"
    - "fully dynamic maximal matching in o log n update time"
    - "weighted matching in the semi streaming model"

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)