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 "expectations or guarantees i want it all a crossroad between games and mdps"):
  Abstract: When reasoning about the strategic capabilities of an agent, it is important to consider the nature of its adversaries. In the particular context of controller synthesis for quantitative specifications, the usual problem is to devise a strategy for a reactive system which yields some desired performance, taking into account the possible impact of the environment of the system. There are at least two ways to look at this environment. In the classical analysis of two-player quantitative games, the environment is purely antagonistic and the problem is to provide strict performance guarantees. In Markov decision processes, the environment is seen as purely stochastic: the aim is then to optimize the expected payoff, with no guarantee on individual outcomes. In this expository work, we report on recent results [10, 9] introducing the beyond worst-case synthesis problem, which is to construct strategies that guarantee some quantitative requirement in the worst-case while providing an higher expected value against a particular stochastic model of the environment given as input. This problem is relevant to produce system controllers that provide nice expected performance in the everyday situation while ensuring a strict (but relaxed) performance threshold even in the event of very bad (while unlikely) circumstances. It has been studied for both the mean-payoff and the shortest path quantitative measures.

Includes members:
  Member "trading performance for stability in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "life is random time is not markov decision processes with window objectives")
  Member "looking at mean payoff and total payoff through windows":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "games where you can play optimally with finite memory")
  Member "solvency markov decision processes with interest":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "percentile queries in multi dimensional markov decision processes")
  Member "mean payoff games with incomplete information":
    also connected through other hyperedges:
      (none)
  Member "meet your expectations with guarantees beyond worst case synthesis in quantitative games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "games where you can play optimally with finite memory")
  Member "strategy synthesis for multi dimensional quantitative objectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "games where you can play optimally with finite memory")
  Member "mean variance optimization in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how should a robot assess risk towards an axiomatic theory of risk in robotics")
  Member "generalized mean payoff and energy games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deciding fast termination for probabilistic vass with nondeterminism")

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)