human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "submodular stochastic probing on matroids":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning optimal search algorithms from data")
  Member "submodular function maximization via the multilinear relaxation and contention resolution schemes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "approximation algorithms for correlated knapsacks and non martingale bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predict and match prophet inequalities with uncertain supply")

Question: Please tell me which class the source paper 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)