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 "maximizing expected utility for stochastic combinatorial optimization problems"):
  Abstract: We study the stochastic versions of a broad class of combinatorial problems where the weights of the elements in the input dataset are uncertain. The class of problems that we study includes shortest paths, minimum weight spanning trees, and minimum weight matchings over probabilistic graphs, and other combinatorial problems like knapsack. We observe that the expected value is inadequate in capturing different types of {\em risk-averse} or {\em risk-prone} behaviors, and instead we consider a more general objective which is to maximize the {\em expected utility} of the solution for some given utility function, rather than the expected weight (expected weight becomes a special case). We show that we can obtain a polynomial time approximation algorithm with {\em additive error} $\epsilon$ for any $\epsilon>0$, if there is a pseudopolynomial time algorithm for the {\em exact} version of the problem (This is true for the problems mentioned above)and the maximum value of the utility function is bounded by a constant. Our result generalizes several prior results on stochastic shortest path, stochastic spanning tree, and stochastic knapsack. Our algorithm for utility maximization makes use of the separability of exponential utility and a technique to decompose a general utility function into exponential utility functions, which may be useful in other stochastic optimization problems.

Includes members:
  Member "stochastic combinatorial optimization via poisson approximation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the markovian price of information")
  Member "when lp is the cure for your matching woes improved bounds for stochastic matchings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predict and match prophet inequalities with uncertain supply")
  Member "when lp is the cure for your matching woes approximating stochastic matchings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the query commit problem")
  Member "a unified approach to ranking in probabilistic databases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uncertainty annotated databases a lightweight approach for approximating certain answers extended version")
  Member "adaptive uncertainty resolution in bayesian combinatorial optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the markovian price of information")
  Member "consensus answers for queries over probabilistic databases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "store location selection via mining search query logs of baidu maps")
  Member "stochastic combinatorial optimization under probabilistic constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic combinatorial optimization via poisson approximation")

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)