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: "improved approximation guarantees for weighted matching in the semi streaming model"
  Abstract: We study the maximum weight matching problem in the semi-streaming model, and improve on the currently best one-pass algorithm due to Zelke [Proceedings of the 25th Annual Symposium on Theoretical Aspects of Computer Science, 2008, pp. 669–680] by devising a deterministic approach whose performance guarantee is 4.91+e. In addition, we study preemptive online algorithms, a class of algorithms related to one-pass semi-streaming algorithms, where we are allowed to maintain only a feasible matching in memory at any point in time. We provide a lower bound of 4.967 on the competitive ratio of any such deterministic algorithm, and hence show that future improvements will have to store in memory a set of edges that is not necessarily a feasible matching. We conclude by presenting an empirical study, conducted in order to compare the practical performance of our approach to that of previously suggested algorithms.

Connected through hyperedge HE-1 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples"):
  with members:
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "extractor based time space lower bounds for learning"
    - "estimating graph parameters from random order streams"
    - "testable bounded degree graph properties are random order streamable"
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"

Connected through hyperedge HE-2 (induced by source paper "distributed weighted matching via randomized composable coresets"):
  with members:
    - "online matching with general arrivals"
    - "weighted matchings via unweighted augmentations"
    - "submodular optimization in the mapreduce model"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "round compression for parallel matching algorithms"
    - "on estimating maximum matching size in graph streams"
    - "almost optimal stochastic weighted matching with few queries"

Connected through hyperedge HE-3 (induced by source paper "approximate maximum matching in random streams"):
  with members:
    - "weighted matchings via unweighted augmentations"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "maximum matching in two three and a few more passes over graph streams"
    - "simplified and space optimal semi streaming for 2 epsilon approximate matching"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "fully dynamic matching in bipartite graphs"
    - "better bounds for matchings in the streaming model"
    - "maximum matching in semi streaming with few passes"

Connected through hyperedge HE-4 (induced by source paper "simulating random walks on graphs in the streaming model"):
  with members:
    - "better bounds for matchings in the streaming model"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "finding sparse cuts locally using evolving sets"

Connected through hyperedge HE-5 (induced by source paper "on conceptually simple algorithms for variants of online bipartite matching"):
  with members:
    - "an experimental study of algorithms for online bipartite matching"
    - "on the limitations of deterministic de randomizations for online bipartite matching and max sat"
    - "computing maximum flow with augmenting electrical flows"
    - "on the power of advice and randomization for online bipartite matching"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "deterministic algorithms for submodular maximization problems"
    - "greedy matching guarantees and limitations"
    - "greedy algorithms make efficient mechanisms"

Connected through hyperedge HE-6 (induced by source paper "on estimating maximum matching size in graph streams"):
  with members:
    - "on approximating functions of the singular values in a stream"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "on the communication complexity of linear algebraic problems in the message passing model"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "maximum matching in semi streaming with few passes"

Connected through hyperedge HE-7 (induced by source paper "communication complexity of approximate maximum matching in the message passing model"):
  with members:
    - "welfare maximization with limited interaction"
    - "maximum matching in turnstile streams"
    - "economic efficiency requires interaction"
    - "when distributed computation is communication expensive"
    - "better bounds for matchings in the streaming model"
    - "laminar families and metric embeddings non bipartite maximum matching problem in the semi streaming model"
    - "maximum matching in semi streaming with few passes"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-8 (induced by source paper "maximum matching in two three and a few more passes over graph streams"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "streaming algorithms for submodular function maximization"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "better bounds for matchings in the streaming model"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "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)