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 "adaptivity gaps for stochastic probing submodular and xos functions"):
  Abstract: Suppose we are given a submodular function $f$ over a set of elements, and we want to maximize its value subject to certain constraints. Good approximation algorithms are known for such problems under both monotone and non-monotone submodular functions. We consider these problems in a stochastic setting, where elements are not all active and we can only get value from active elements. Each element $e$ is active independently with some known probability $p_e$, but we don't know the element's status \emph{a priori}. We find it out only when we \emph{probe} the element $e$---probing reveals whether it's active or not, whereafter we can use this information to decide which other elements to probe. Eventually, if we have a probed set $S$ and a subset $\text{active}(S)$ of active elements in $S$, we can pick any $T \subseteq \text{active}(S)$ and get value $f(T)$. Moreover, the sequence of elements we probe must satisfy a given \emph{prefix-closed constraint}---e.g., these may be given by a matroid, or an orienteering constraint, or deadline, or precedence constraint, or an arbitrary downward-closed constraint---if we can probe some sequence of elements we can probe any prefix of it. What is a good strategy to probe elements to maximize the expected value? #R##N#In this paper we study the gap between adaptive and non-adaptive strategies for $f$ being a submodular or a fractionally subadditive (XOS) function. If this gap is small, we can focus on finding good non-adaptive strategies instead, which are easier to find as well as to represent. We show that the adaptivity gap is a constant for monotone and non-monotone submodular functions, and logarithmic for XOS functions of small \emph{width}. These bounds are nearly tight. Our techniques show new ways of arguing about the optimal adaptive decision tree for stochastic problems.

Includes members:
  Member "improved approximation algorithms for stochastic matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the markovian price of information")
  Member "submodular stochastic probing on matroids":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning optimal search algorithms from data")
  Member "on the adaptivity gap of stochastic orienteering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the markovian price of information")
  Member "a stochastic probing problem with applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning optimal search algorithms from data")
  Member "stochastic combinatorial optimization via poisson approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the markovian price of information")
  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")

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)