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: "near optimal adaptivity gaps for stochastic multi value probing"
  Abstract: Consider a kidney-exchange application where we want to find a max-matching in a random graph. To find whether an edge $e$ exists, we need to perform an expensive test, in which case the edge $e$ appears independently with a \emph{known} probability $p_e$. Given a budget on the total cost of the tests, our goal is to find a testing strategy that maximizes the expected maximum matching size. #R##N#The above application is an example of the stochastic probing problem. In general the optimal stochastic probing strategy is difficult to find because it is \emph{adaptive}---decides on the next edge to probe based on the outcomes of the probed edges. An alternate approach is to show the \emph{adaptivity gap} is small, i.e., the best \emph{non-adaptive} strategy always has a value close to the best adaptive strategy. This allows us to focus on designing non-adaptive strategies that are much simpler. Previous works, however, have focused on Bernoulli random variables that can only capture whether an edge appears or not. In this work we introduce a multi-value stochastic probing problem, which can also model situations where the weight of an edge has a probability distribution over multiple values. #R##N#Our main technical contribution is to obtain (near) optimal bounds for the (worst-case) adaptivity gaps for multi-value stochastic probing over prefix-closed constraints. For a monotone submodular function, we show the adaptivity gap is at most $2$ and provide a matching lower bound. For a weighted rank function of a $k$-extendible system (a generalization of intersection of $k$ matroids), we show the adaptivity gap is between $O(k\log k)$ and $k$. None of these results were known even in the Bernoulli case where both our upper and lower bounds also apply, thereby resolving an open question of Gupta et al.

Connected through hyperedge HE-1 (induced by source paper "pandora s box problem with order constraints"):
  with members:
    - "pandora s problem with nonobligatory inspection"
    - "generalizing complex hypotheses on product distributions auctions prophet inequalities and pandora s problem"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "guess free maximization of submodular and linear sums"
    - "the price of information in combinatorial optimization"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

Connected through hyperedge HE-2 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback"):
  with members:
    - "adaptive influence maximization with myopic feedback"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "adaptive influence maximization in dynamic social networks"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "boosting information spread an algorithmic approach"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"

Connected through hyperedge HE-3 (induced by source paper "adaptive influence maximization with myopic feedback"):
  with members:
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "adaptive influence maximization under general feedback models"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "adaptive influence maximization in dynamic social networks"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "adaptive robust optimization with nearly submodular structure"):
  with members:
    - "robust monotone submodular function maximization"
    - "robust submodular maximization offline and online algorithms"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "robust influence maximization"
    - "deterministic algorithms for submodular maximization problems"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "price of dependence stochastic submodular maximization with dependent items"):
  with members:
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "no time to observe adaptive influence maximization with partial feedback"
    - "discrete stochastic submodular maximization adaptive vs non adaptive vs offline"
    - "submodular stochastic probing on matroids"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)