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: "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
  Abstract: We present MultiGain, a tool to synthesize strategies for Markov decision processes (MDPs) with multiple mean-payoff objectives. Our models are described in PRISM, and our tool uses the existing interface and simulator of PRISM. Our tool extends PRISM by adding novel algorithms for multiple mean-payoff objectives, and also provides features such as (i)~generating strategies and exploring them for simulation, and checking them with respect to other properties; and (ii)~generating an approximate Pareto curve for two mean-payoff objectives. In addition, we present a new practical algorithm for the analysis of MDPs with multiple mean-payoff objectives under memoryless strategies.

Connected through hyperedge HE-1 (induced by source paper "approximating values of generalized reachability stochastic games"):
  with members:
    - "efficient strategy iteration for mean payoff in markov decision processes"
    - "decidability results for multi objective stochastic games"
    - "perfect information stochastic games with generalized mean payoff objectives"
    - "verification of markov decision processes using learning algorithms"
    - "percentile queries in multi dimensional markov decision processes"
    - "robust multidimensional mean payoff games are undecidable"
    - "trading performance for stability in markov decision processes"
    - "pareto curves for probabilistic model checking"

Connected through hyperedge HE-2 (induced by source paper "efficient strategy iteration for mean payoff in markov decision processes"):
  with members:
    - "value iteration for long run average reward in markov decision processes"
    - "efficient parallel strategy improvement for parity games"
    - "synthesising strategy improvement and recursive algorithms for solving 2 5 player parity games"
    - "verification of markov decision processes using learning algorithms"
    - "parametric strategy iteration"
    - "assume guarantee abstraction refinement for probabilistic systems"
    - "using strategy improvement to stay alive"
    - "the complexity of solving reachability games using value and strategy iteration"

Connected through hyperedge HE-3 (induced by source paper "probabilistic motion planning under temporal tasks and soft constraints"):
  with members:
    - "risk averse ω regular markov decision process control"
    - "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"
    - "control of noisy differential drive vehicles from time bounded temporal logic specifications"

Connected through hyperedge HE-4 (induced by source paper "value iteration for long run average reward in markov decision processes"):
  with members:
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "knowledge spaces and the completeness of learning strategies"
    - "the complexity of ergodic mean payoff games"
    - "verification of markov decision processes using learning algorithms"
    - "markov decision processes with multiple long run averageobjectives"
    - "faster algorithms for markov decision processes with low treewidth"
    - "trading performance for stability in markov decision processes"
    - "assume guarantee abstraction refinement for probabilistic systems"

Connected through hyperedge HE-5 (induced by source paper "quantitative automata under probabilistic semantics"):
  with members:
    - "nested weighted limit average automata of bounded width"
    - "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"
    - "energy and mean payoff parity markov decision processes"
    - "measuring and synthesizing systems in probabilistic environments"
    - "expressiveness and closure properties for quantitative languages"

Connected through hyperedge HE-6 (induced by source paper "rational verification in iterated electric boolean games"):
  with members:
    - "non zero sum games for reactive synthesis"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "multiplayer cost games with simple nash equilibria"
    - "energy parity games"

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.AI(Artificial Intelligence)