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 "markov decision processes with multiple long run averageobjectives"):
  Abstract: We study Markov decision processes (MDPs) with multiple#N#limit-average (or mean-payoff) functions. We consider two#N#different objectives, namely, expectation and satisfaction#N#objectives. Given an MDP with k limit-average functions, in the#N#expectation objective the goal is to maximize the expected#N#limit-average value, and in the satisfaction objective the goal#N#is to maximize the probability of runs such that the#N#limit-average value stays above a given vector. We show that#N#under the expectation objective, in contrast to the case of one#N#limit-average function, both randomization and memory are#N#necessary for strategies even for epsilon-approximation, and#N#that finite-memory randomized strategies are sufficient for#N#achieving Pareto optimal values. Under the satisfaction#N#objective, in contrast to the case of one limit-average#N#function, infinite memory is necessary for strategies achieving#N#a specific value (i.e. randomized finite-memory strategies are#N#not sufficient), whereas memoryless randomized strategies are#N#sufficient for epsilon-approximation, for all epsilon>0. We#N#further prove that the decision problems for both expectation#N#and satisfaction objectives can be solved in polynomial time#N#and the trade-off curve (Pareto curve) can be#N#epsilon-approximated in time polynomial in the size of the MDP#N#and 1/epsilon, and exponential in the number of limit-average#N#functions, for all epsilon>0. Our analysis also reveals flaws#N#in previous work for MDPs with multiple mean-payoff functions#N#under the expectation objective, corrects the flaws, and allows#N#us to obtain improved results.

Includes members:
  Member "one counter stochastic games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "computing expected runtimes for constant probability programs")
  Member "multi objective model checking of markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the probabilistic model checker storm")

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.GT(Computer Science and Game Theory)