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: "energy and mean payoff parity markov decision processes"
  Abstract: We consider Markov Decision Processes (MDPs) with mean-payoff parity and energy parity objectives. In system design, the parity objective is used to encode \omega-regular specifications, and the mean-payoff and energy objectives can be used to model quantitative resource constraints. The energy condition requires that the resource level never drops below 0, and the mean-payoff condition requires that the limit-average value of the resource consumption is within a threshold. While these two (energy and mean-payoff) classical conditions are equivalent for two-player games, we show that they differ for MDPs. We show that the problem of deciding whether a state is almost-sure winning (i.e., winning with probability 1) in energy parity MDPs is in NP \cap coNP, while for mean-payoff parity MDPs, the problem is solvable in polynomial time, improving a recent PSPACE bound.

Connected through hyperedge HE-1 (induced by source paper "stochastic shortest paths and weight bounded properties in markov decision processes"):
  with members:
    - "mdps with energy parity objectives"
    - "optimizing the expected mean payoff in energy markov decision processes"
    - "qualitative analysis of vass induced mdps"
    - "variations on the stochastic shortest path problem"
    - "the odds of staying on budget"
    - "computing quantiles in markov reward models"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"
    - "energy parity games"

Connected through hyperedge HE-2 (induced by source paper "probabilistic motion planning under temporal tasks and soft constraints"):
  with members:
    - "risk averse ω regular markov decision process control"
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "pareto efficiency in synthesizing shared autonomy policies with temporal logic constraints"
    - "variations on the stochastic shortest path problem"
    - "percentile queries in multi dimensional markov decision processes"
    - "a survey of multi objective sequential decision making"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"
    - "pareto curves for probabilistic model checking"

Connected through hyperedge HE-3 (induced by source paper "mdps with energy parity objectives"):
  with members:
    - "optimizing the expected mean payoff in energy markov decision processes"
    - "qualitative analysis of vass induced mdps"
    - "solving parity games on integer vectors"
    - "energy parity games"
    - "better quality in synthesis through quantitative objectives"

Connected through hyperedge HE-4 (induced by source paper "optimizing the expected mean payoff in energy markov decision processes"):
  with members:
    - "an omega algebra for real time energy problems"
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "qualitative analysis of vass induced mdps"
    - "average energy games"
    - "multidimensional beyond worst case and almost sure problems for mean payoff objectives"
    - "zero reachability in probabilistic multi counter automata"
    - "the odds of staying on budget"
    - "polynomial time algorithms for energy games with special weight structures"

Connected through hyperedge HE-5 (induced by source paper "quantitative automata under probabilistic semantics"):
  with members:
    - "nested weighted limit average automata of bounded width"
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "nested weighted automata"
    - "markov decision processes with multiple long run averageobjectives"
    - "discounting in ltl"
    - "measuring and synthesizing systems in probabilistic environments"
    - "expressiveness and closure properties for quantitative languages"

Connected through hyperedge HE-6 (induced by source paper "stochastic shortest path with energy constraints in pomdps"):
  with members:
    - "counterexample explanation by learning small strategies in markov decision processes"
    - "optimal cost almost sure reachability in pomdps"
    - "solving pomdps by searching the space of finite policies"
    - "efficient controller synthesis for consumption games with multiple resource types"
    - "heuristic search value iteration for pomdps"
    - "qualitative analysis of partially observable markov decision processes"

Connected through hyperedge HE-7 (induced by source paper "high quality synthesis against stochastic environments"):
  with members:
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"
    - "better quality in synthesis through quantitative objectives"
    - "environment assumptions for synthesis"

Connected through hyperedge HE-8 (induced by source paper "on frequency ltl in probabilistic systems"):
  with members:
    - "a probabilistic temporal logic with frequency operators and its model checking"
    - "better quality in synthesis through 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)