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: "almost optimal stochastic weighted matching with few queries"
  Abstract: We consider the stochastic matching problem. An edge-weighted general graph $G(V, E)$ is given in the input, where each edge in $E$ is realized independently with probability $p$. The realization is initially unknown, however, we are able to query the edges to determine whether they are realized. The goal is to query only a small number of edges to find a realized matching that is sufficiently close to the optimum (i.e., the maximum matching among all realized edges). #R##N#The stochastic matching problem has been studied extensively during the past decade because of its numerous real-world applications in kidney-exchange, matchmaking services, online labor markets, advertisement, etc. #R##N#Our main result is an adaptive algorithm that, in expectation, finds a $(1-\epsilon)$-approximation by querying only $O(1)$ edges per vertex. Prior to our work, no nontrivial approximation was known for weighted graphs using a constant per-vertex budget. The only known result for weighted graphs is the algorithm of Maehara and Yamaguchi~(SODA'18) that achieves a $(1-\epsilon)$-approximation by querying $\Theta(w\log{n})$ edges per vertex where $w$ is the maximum edge-weight. Our result is a substantial improvement over this bound and has an appealing practical message: No matter what the structure of the input graph is, one can get arbitrarily close to the optimum solution by querying only a constant number of edges per vertex. #R##N#To obtain our results, we introduce novel properties of weighted augmenting paths that may be of independent interest in generalizing augmenting path based techniques to weighted graphs.

Connected through hyperedge HE-1 (induced by source paper "stochastic matching with few queries 1 varepsilon approximation"):
  with members:
    - "stochastic monotone submodular maximization with queries"
    - "towards a unified theory of sparsification for matching problems"
    - "stochastic matching with few queries new algorithms and tools"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "opting into optimal matchings"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "better bounds for matchings in the 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"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"

Connected through hyperedge HE-3 (induced by source paper "beating greedy for stochastic bipartite matching"):
  with members:
    - "the price of information in combinatorial optimization"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "prophet secretary for combinatorial auctions and matroids"
    - "stochastic packing integer programs with few queries"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "improved approximation algorithms for stochastic matching"
    - "the query commit problem"

Connected through hyperedge HE-4 (induced by source paper "stochastic monotone submodular maximization with queries"):
  with members:
    - "stochastic matching with few queries new algorithms and tools"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"

Connected through hyperedge HE-5 (induced by source paper "towards a unified theory of sparsification for matching problems"):
  with members:
    - "stochastic matching with few queries new algorithms and tools"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "preserving distances in very faulty graphs"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "on estimating maximum matching size in graph streams"
    - "stochastic packing integer programs with few queries"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "fully dynamic matching in bipartite graphs"

Connected through hyperedge HE-6 (induced by source paper "stochastic packing integer programs with few queries"):
  with members:
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "generalized hypergraph matching via iterated packing and local ratio"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"
    - "iterative packing for demand and hypergraph matching"
    - "the query commit problem"

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)