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 ell_p load balancing and moment problems via the l function method"
  Abstract: This paper considers stochastic optimization problems whose objective functions involve powers of random variables. For example, consider the classic Stochastic lp Load Balancing Problem (SLBp): There are $m$ machines and $n$ jobs, and known independent random variables $Y_{ij}$ decribe the load incurred on machine $i$ if we assign job $j$ to it. The goal is to assign each jobs to machines in order to minimize the expected $l_p$-norm of the total load on the machines. While convex relaxations represent one of the most powerful algorithmic tools, in problems such as SLBp the main difficulty is to capture the objective function in a way that only depends on each random variable separately. #R##N#We show how to capture $p$-power-type objectives in such separable way by using the $L$-function method, introduced by Lata{\l}a to relate the moment of sums of random variables to the individual marginals. We show how this quickly leads to a constant-factor approximation for very general subset selection problem with $p$-moment objective. #R##N#Moreover, we give a constant-factor approximation for SLBp, improving on the recent $O(p/\ln p)$-approximation of [Gupta et al., SODA 18]. Here the application of the method is much more involved. In particular, we need to sharply connect the expected $l_p$-norm of a random vector with the $p$-moments of its marginals (machine loads), taking into account simultaneously the different scales of the loads that are incurred by an unknown assignment.

Connected through hyperedge HE-1 (induced by source paper "stochastic load balancing on unrelated machines"):
  with members:
    - "stochastic combinatorial optimization via poisson approximation"
    - "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)