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: "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.

Connected through hyperedge HE-1 (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"
    - "stochastic combinatorial optimization via poisson approximation"

Connected through hyperedge HE-2 (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"
    - "stochastic combinatorial optimization via poisson approximation"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "the burden of risk aversion in mean risk selfish routing"):
  with members:
    - "congestion games with distance based strict uncertainty"
    - "on the hardness of signaling"
    - "prior independent auctions for risk averse agents"
    - "mechanisms for risk averse agents without loss"

Connected through hyperedge HE-4 (induced by source paper "a fully polynomial time approximation scheme for approximating a sum of random variables"):
  with members:
    - "stochastic combinatorial optimization via poisson approximation"
    - "a deterministic polynomial time approximation scheme for counting knapsack solutions"

Connected through hyperedge HE-5 (induced by source paper "a polynomial time approximation scheme for fault tolerant distributed storage"):
  with members:
    - "stochastic combinatorial optimization via poisson approximation"
    - "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"
    - "symmetric allocations for distributed storage"
    - "memory allocation in distributed storage networks"
    - "improved approximation of linear threshold functions"
    - "bounded independence fools halfspaces"
    - "pseudorandom generators for polynomial threshold functions"

Connected through hyperedge HE-6 (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"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "stochastic combinatorial optimization under probabilistic constraints"
    - "computing equilibria in anonymous games"

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)