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: "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.

Connected through hyperedge HE-1 (induced by source paper "reconciling rationality and stochasticity rich behavioral models in two player games"):
  with members:
    - "average energy games"
    - "non zero sum games for reactive synthesis"
    - "variations on the stochastic shortest path problem"
    - "the odds of staying on budget"
    - "percentile queries in multi dimensional markov decision processes"
    - "computing quantiles in markov reward models"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"
    - "strategy synthesis for multi dimensional quantitative objectives"

Connected through hyperedge HE-2 (induced by source paper "non zero sum games for reactive synthesis"):
  with members:
    - "weak subgame perfect equilibria and their application to quantitative reachability"
    - "assume admissible synthesis"
    - "reactive synthesis without regret"
    - "multidimensional beyond worst case and almost sure problems for mean payoff objectives"
    - "variations on the stochastic shortest path problem"
    - "secure equilibria in weighted games"
    - "percentile queries in multi dimensional markov decision processes"
    - "how to handle assumptions in synthesis"

Connected through hyperedge HE-3 (induced by source paper "variations on the stochastic shortest path problem"):
  with members:
    - "quantitative languages defined by functional automata"
    - "multiple environment markov decision processes"
    - "the odds of staying on budget"
    - "percentile queries in multi dimensional markov decision processes"
    - "to reach or not to reach efficient algorithms for total payoff games"
    - "what is decidable about partially observable markov decision processes with omega regular objectives"
    - "looking at mean payoff and total payoff through windows"
    - "computing quantiles in markov reward models"

Connected through hyperedge HE-4 (induced by source paper "meet your expectations with guarantees beyond worst case synthesis in quantitative games"):
  with members:
    - "the complexity of the kth largest subset problem and related problems"
    - "variations on the stochastic shortest path problem"
    - "symblicit algorithms for optimal strategy synthesis in monotonic markov decision processes"
    - "percentile queries in multi dimensional markov decision processes"
    - "trading performance for stability in markov decision processes"
    - "looking at mean payoff and total payoff through windows"
    - "the complexity of multi mean payoff and multi energy games"
    - "strategy synthesis for multi dimensional quantitative objectives"

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.GT(Computer Science and Game Theory)