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: "stochastic combinatorial optimization under probabilistic constraints"
  Abstract: AbstractIn this paper, we present approximation algorithms for combinatorial optimization problemsunder probabilistic constraints. Speciﬁcally, we focus on stochastic variants of two importantcombinatorial optimization problems: the k-center problem and the set cover problem, with un-certainty characterized by a probability distribution over set of points or elements to be covered.We consider these problems under adaptive and non-adaptive settings, and present eﬃcient ap-proximation algorithms for the case when underlying distribution is a product distribution. Incontrast to the expected cost model prevalent in stochastic optimization literature, our problemdeﬁnitions support restrictions on the probability distributions of the total costs, via incorporat-ing constraints that bound the probability with which the incurred costs may exceed a giventhreshold. 1 Introduction A prevalent model to deal with uncertain data in optimization problems is to minimize expectedcost over an input probability distribution. However, the expected cost model does not adequatelycapture the following two aspects of the problem. Firstly, in many applications, constraint violationscannot be modeled by costs or penalties in any reasonable way (e.g., safety relevant restrictionslike levels of a water reservoir). Thus, if the problem constraints involve an uncertain parameter,one would rather insist on bounding the probability that a decision is infeasible. This leads to

Connected through hyperedge HE-1 (induced by source paper "stochastic combinatorial optimization via poisson approximation"):
  with members:
    - "matroid prophet inequalities"
    - "advances on matroid secretary problems free order model and laminar case"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "maximizing expected utility for stochastic combinatorial optimization problems"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "computing equilibria in anonymous games"

Connected through hyperedge HE-2 (induced by source paper "maximizing expected utility for stochastic combinatorial optimization problems"):
  with members:
    - "stochastic combinatorial optimization via poisson approximation"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "when lp is the cure for your matching woes approximating stochastic matchings"
    - "a unified approach to ranking in probabilistic databases"
    - "adaptive uncertainty resolution in bayesian combinatorial optimization problems"
    - "consensus answers for queries over probabilistic databases"

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)