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: "non negative submodular stochastic probing via stochastic contention resolution schemes"
  Abstract: The abstract model of stochastic probing was presented by Gupta and Nagarajan (IPCO'13), and provides a unified view of a number of problems. Adamczyk, Sviridenko, Ward (STACS'14) gave better approximation for matroid environments and linear objectives. At the same time this method was easily extendable to settings, where the objective function was monotone submodular. However, the case of non-negative submodular function could not be handled by previous techniques. In this paper we address this problem, and our results are twofold. First, we adapt the notion of contention resolution schemes of Chekuri, Vondr\'ak, Zenklusen (SICOMP'14) to show that we can optimize non-negative submodular functions in this setting with a constant factor loss with respect to the deterministic setting. Second, we show a new contention resolution scheme for transversal matroids, which yields better approximations in the stochastic probing setting than the previously known tools. The rounding procedure underlying the scheme can be of independent interest --- Bansal, Gupta, Li, Mestre, Nagarajan, Rudra (Algorithmica'12) gave two seemingly different algorithms for stochastic matching and stochastic $k$-set packing problems with two different analyses, but we show that our single technique can be used to analyze both their algorithms.

Connected through hyperedge HE-1 (induced by source paper "an optimal monotone contention resolution scheme for bipartite matchings via a polyhedral viewpoint"):
  with members:
    - "understanding the correlation gap for matchings"
    - "submodular maximization through the lens of linear programming"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization beyond 1 e"
    - "online contention resolution schemes"
    - "a stochastic probing problem with applications"
    - "a k 3 2 approximation algorithm for monotone submodular k set packing and general k exchange systems"
    - "symmetry and approximability of submodular maximization problems"

Connected through hyperedge HE-2 (induced by source paper "submodular stochastic probing with prices"):
  with members:
    - "random order contention resolution schemes"
    - "the price of information in combinatorial optimization"
    - "online contention resolution schemes"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-3 (induced by source paper "algorithms to approximate column sparse packing problems"):
  with members:
    - "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"
    - "random order contention resolution schemes"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "improved algorithmic bounds for discrepancy of sparse set systems"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "improved approximation algorithms for stochastic matching"

Connected through hyperedge HE-4 (induced by source paper "improved approximation algorithms for stochastic matching"):
  with members:
    - "submodular stochastic probing on matroids"
    - "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)