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 "meet your expectations with guarantees beyond worst case synthesis in quantitative games"):
  Abstract: We extend the quantitative synthesis framework by going beyond the worst-case. On the one hand, classical analysis of two-player games involves an adversary (modeling the environment of the system) which is purely antagonistic and asks for strict guarantees. On the other hand, stochastic models like Markov decision processes represent situations where the system is faced to a purely randomized environment: the aim is then to optimize the expected payoff, with no guarantee on individual outcomes. We introduce 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. We study the beyond worst-case synthesis problem for two important quantitative settings: the mean-payoff and the shortest path. In both cases, we show how to decide the existence of finite-memory strategies satisfying the problem and how to synthesize one if one exists. We establish algorithms and we study complexity bounds and memory requirements.

Includes members:
  Member "the complexity of the kth largest subset problem and related problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "expected window mean payoff")
  Member "variations on the stochastic shortest path problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning of risk constrained policies in markov decision processes")
  Member "symblicit algorithms for optimal strategy synthesis in monotonic markov decision processes":
    also connected through other hyperedges:
      (none)
  Member "percentile queries in multi dimensional markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "life is random time is not markov decision processes with window objectives")
  Member "expectations or guarantees i want it all a crossroad between games and mdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reconciling rationality and stochasticity rich behavioral models in two player games")
  Member "trading performance for stability in markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "games where you can play optimally with finite memory")
  Member "the complexity of multi mean payoff and multi energy games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "games where you can play optimally with finite memory")

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)