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 "submodular stochastic probing on matroids"):
  Abstract: In a stochastic probing problem we are given a universe $E$, where each element $e \in E$ is active independently with probability $p_e$, and only a probe of e can tell us whether it is active or not. On this universe we execute a process that one by one probes elements --- if a probed element is active, then we have to include it in the solution, which we gradually construct. Throughout the process we need to obey inner constraints on the set of elements taken into the solution, and outer constraints on the set of all probed elements. This abstract model was presented by Gupta and Nagarajan (IPCO '13), and provides a unified view of a number of problems. Thus far, all the results falling under this general framework pertain mainly to the case in which we are maximizing a linear objective function of the successfully probed elements. In this paper we generalize the stochastic probing problem by considering a monotone submodular objective function. We give a $(1 - 1/e)/(k_{in} + k_{out}+1)$-approximation algorithm for the case in which we are given $k_{in}$ matroids as inner constraints and $k_{out}$ matroids as outer constraints. Additionally, we obtain an improved $1/(k_{in} + k_{out})$-approximation algorithm for linear objective functions.

Includes members:
  Member "bounds on the lambert function and their application to the outage analysis of user cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "thompson sampling for combinatorial network optimization in unknown environments")
  Member "a stochastic probing problem with applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning optimal search algorithms from data")
  Member "matroid prophet inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predict and match prophet inequalities with uncertain supply")
  Member "adaptive submodular optimization under matroid constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on maximizing a monotone k submodular function subject to a matroid constraint")
  Member "submodular function maximization via the multilinear relaxation and contention resolution schemes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "predict and match prophet inequalities with uncertain supply")
  Member "mechanism design via correlation gap":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "when lp is the cure for your matching woes improved bounds for stochastic matchings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)