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 via poisson approximation"
  Abstract: We study several stochastic combinatorial problems, including the expected utility maximization problem, the stochastic knapsack problem and the stochastic bin packing problem. A common technical challenge in these problems is to optimize some function of the sum of a set of random variables. The difficulty is mainly due to the fact that the probability distribution of the sum is the convolution of a set of distributions, which is not an easy objective function to work with. To tackle this difficulty, we introduce the Poisson approximation technique. The technique is based on the Poisson approximation theorem discovered by Le Cam, which enables us to approximate the distribution of the sum of a set of random variables using a compound Poisson distribution. #R##N#We first study the expected utility maximization problem introduced recently [Li and Despande, FOCS11]. For monotone and Lipschitz utility functions, we obtain an additive PTAS if there is a multidimensional PTAS for the multi-objective version of the problem, strictly generalizing the previous result. #R##N#For the stochastic bin packing problem (introduced in [Kleinberg, Rabani and Tardos, STOC97]), we show there is a polynomial time algorithm which uses at most the optimal number of bins, if we relax the size of each bin and the overflow probability by eps. #R##N#For stochastic knapsack, we show a 1+eps-approximation using eps extra capacity, even when the size and reward of each item may be correlated and cancelations of items are allowed. This generalizes the previous work [Balghat, Goel and Khanna, SODA11] for the case without correlation and cancelation. Our algorithm is also simpler. We also present a factor 2+eps approximation algorithm for stochastic knapsack with cancelations. the current known approximation factor of 8 [Gupta, Krishnaswamy, Molinaro and Ravi, FOCS11].

Connected through hyperedge HE-1 (induced by source paper "the markovian price of information"):
  with members:
    - "the price of information in combinatorial optimization"
    - "optimal data acquisition for statistical estimation"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "descending price optimally coordinates search"
    - "descending price coordinates approximately efficient search"
    - "online contention resolution schemes"
    - "improved approximation algorithms for stochastic matching"
    - "submodular stochastic probing on matroids"

Connected through hyperedge HE-2 (induced by source paper "near optimal adaptivity gaps for stochastic multi value probing"):
  with members:
    - "a ptas for a class of stochastic dynamic programs"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "combinatorial prophet inequalities"
    - "discrete stochastic submodular maximization adaptive vs non adaptive vs offline"
    - "improved approximation algorithms for stochastic matching"
    - "approximation algorithms for stochastic boolean function evaluation and stochastic submodular set cover"
    - "submodular stochastic probing on matroids"
    - "on the adaptivity gap of stochastic orienteering"

Connected through hyperedge HE-3 (induced by source paper "an fptas for stochastic unbounded min knapsack problem"):
  with members:
    - "a faster fptas for the unbounded knapsack problem"
    - "a fully polynomial time approximation scheme for approximating a sum of random variables"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"

Connected through hyperedge HE-4 (induced by source paper "stochastic load balancing on unrelated machines"):
  with members:
    - "stochastic ell_p load balancing and moment problems via the l function method"
    - "maximizing expected utility for stochastic combinatorial optimization problems"

Connected through hyperedge HE-5 (induced by source paper "nearly optimal pricing algorithms for production constrained and laminar bayesian selection"):
  with members:
    - "a ptas for a class of stochastic dynamic programs"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "online contention resolution schemes"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "matroid prophet inequalities"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"

Connected through hyperedge HE-6 (induced by source paper "algorithms to approximate column sparse packing problems"):
  with members:
    - "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"
    - "random order contention resolution schemes"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "improved algorithmic bounds for discrepancy of sparse set systems"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "non negative submodular stochastic probing via stochastic contention resolution schemes"

Connected through hyperedge HE-7 (induced by source paper "the price of information in combinatorial optimization"):
  with members:
    - "descending price coordinates approximately efficient search"
    - "improved approximation algorithms for stochastic matching"
    - "submodular stochastic probing on matroids"
    - "on the adaptivity gap of stochastic orienteering"
    - "a stochastic probing problem with applications"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-8 (induced by source paper "combinatorial multi armed bandit with general reward functions"):
  with members:
    - "combinatorial cascading bandits"
    - "combinatorial bandits revisited"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "maximizing expected utility for stochastic combinatorial optimization problems"

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)