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: "ignorance is almost bliss near optimal stochastic matching with few queries"
  Abstract: The stochastic matching problem deals with finding a maximum matching in a graph whose edges are unknown but can be accessed via queries. This is a special case of stochastic k-set packing, where the problem is to find a maximum packing of sets, each of which exists with some probability. In this paper, we provide edge and set query algorithms for these two problems, respectively, that provably achieve some fraction of the omniscient optimal solution. Our main theoretical result for the stochastic matching (i.e., 2-set packing) problem is the design of an adaptive algorithm that queries only a constant number of edges per vertex and achieves a (1-e) fraction of the omniscient optimal solution, for an arbitrarily small e > 0. Moreover, this adaptive algorithm performs the queries in only a constant number of rounds. We complement this result with a non-adaptive (i.e., one round of queries) algorithm that achieves a (0.5 - e) fraction of the omniscient optimum. We also extend both our results to stochastic k-set packing by designing an adaptive algorithm that achieves a (2/k - e) fraction of the omniscient optimal solution, again with only O(1) queries per element. This guarantee is close to the best known polynomial-time approximation ratio of 3/k+1 -e for the deterministic k-set packing problem [Furer 2013].   We empirically explore the application of (adaptations of) these algorithms to the kidney exchange problem, where patients with end-stage renal failure swap willing but incompatible donors. We show on both generated data and on real data from the first 169 match runs of the UNOS nationwide kidney exchange that even a very small number of non-adaptive edge queries per vertex results in large gains in expected successful matches.

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"
    - "almost optimal stochastic weighted matching with few queries"
    - "opting into optimal matchings"
    - "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"
    - "almost optimal stochastic weighted 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"
    - "almost optimal stochastic weighted matching with few queries"
    - "stochastic packing integer programs with few queries"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "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"
    - "almost optimal stochastic weighted matching with few queries"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"

Connected through hyperedge HE-5 (induced by source paper "stochastic submodular cover with limited adaptivity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "stochastic packing integer programs with few queries"
    - "scenario submodular cover"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "parallel algorithms for select and partition with noisy comparisons"

Connected through hyperedge HE-6 (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"
    - "almost optimal stochastic weighted matching with few queries"
    - "stochastic packing integer programs with few queries"
    - "fully dynamic matching in bipartite graphs"

Connected through hyperedge HE-7 (induced by source paper "stochastic packing integer programs with few queries"):
  with members:
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "almost optimal stochastic weighted matching with few queries"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "the stochastic matching problem beating half with a non adaptive algorithm"):
  with members:
    - "dynamic matching market design"
    - "ranking on arbitrary graphs rematch via continuous lp with monotone and boundary condition constraints"
    - "a stochastic probing problem with applications"
    - "maximum matching in semi streaming with few passes"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

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)