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 with prices"):
  Abstract: We introduce Stochastic Probing with Prices (SPP), a variant of the Stochastic Probing (SP) model in which we must pay a price to probe an element. A SPP problem involves two set systems $(N,\mathcal{I}_{in})$ and $(N,\mathcal{I}_{out})$ where each $e\in N$ is active with probability $p_e$. To discover whether $e$ is active, it must be probed by paying the price $\Delta_e$. If it is probed and active, then it is irrevocably added to the solution. Moreover, at all times, the set of probed elements must lie in $\mathcal{I}_{out}$, and the solution (the set of probed and active elements) must lie in $\mathcal{I}_{in}$. The goal is to maximize a set function $f$ minus the cost of the probes. We show this problem can be approximately solved via multilinear relaxation and contention resolution schemes. If $\mathcal{I}_{in}$ and $\mathcal{I}_{out}$ admit $(b,c_{in})$ and $(b,c_{out})$ contention resolution schemes, we give a $\max\{c_{out} c_{in},c_{out}+c_{in}-1\}\alpha(b)$-approximation to online SPP, where $\alpha(b)=1-e^{-b}$ if $f$ is monotone, and $be^{-b}$ otherwise. These results apply in the online version of the problem: The elements are presented in an arbitrary (and potentially adversarial) order. In the special case when $\mathcal{I}_{in}$ and $\mathcal{I}_{out}$ are the intersection of $k$ and $\ell$ matroids, we demonstrate that the optimal value for $b$ is $\frac{1}{2}(z+2-\sqrt{z(z+4)})$ when $f$ is monotone, and $z+1-W(ze^{z+1})$ when $f$ is non-monotone, where $z=k+\ell$ and $W$ is the lambert W function. Our results also provide state-of-the-art approximations for online SP when $f$ is monotone, and provide the first approximation to online, adversarial SP when $f$ is non-monotone. Finally, we demonstrate that when $f$ is modular there exists an $\alpha$-approximation to SP only if there exists an $\alpha$-approximation to SPP.

Includes members:
  Member "random order contention resolution schemes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal single choice prophet inequalities from samples")
  Member "the price of information in combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pandora s box problem with order constraints")
  Member "online contention resolution schemes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "non negative submodular stochastic probing via stochastic contention resolution schemes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an optimal monotone contention resolution scheme for bipartite matchings via a polyhedral viewpoint")
  Member "submodular stochastic probing on matroids":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning optimal search algorithms from data")
  Member "a stochastic probing problem with applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning optimal search algorithms from data")
  Member "optimal approximation for submodular and supermodular optimization with bounded curvature":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regularized submodular maximization at scale")
  Member "online stochastic matching beating 1 1 e":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online graph matching problems with a worst case reassignment budget")

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)