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: "algorithms for game metrics"
  Abstract: Simulation and bisimulation metrics for stochastic systems provide a#N#quantitative generalization of the classical simulation and bisimulation#N#relations. These metrics capture the similarity of states with respect to#N#quantitative specifications written in the quantitative {\mu}-calculus and#N#related probabilistic logics. We first show that the metrics provide a bound#N#for the difference in long-run average and discounted average behavior across#N#states, indicating that the metrics can be used both in system verification,#N#and in performance evaluation. For turn-based games and MDPs, we provide a#N#polynomial-time algorithm for the computation of the one-step metric distance#N#between states. The algorithm is based on linear programming; it improves on#N#the previous known exponential-time algorithm based on a reduction to the#N#theory of reals. We then present PSPACE algorithms for both the decision#N#problem and the problem of approximating the metric distance between two#N#states, matching the best known algorithms for Markov chains. For the#N#bisimulation kernel of the metric our algorithm works in time O(n^4) for both#N#turn-based games and MDPs; improving the previously best known O(n^9\cdot#N#log(n)) time algorithm for MDPs. For a concurrent game G, we show that#N#computing the exact distance between states is at least as hard as computing#N#the value of concurrent reachability games and the square-root-sum problem in#N#computational geometry. We show that checking whether the metric distance is#N#bounded by a rational r, can be done via a reduction to the theory of real#N#closed fields, involving a formula with three quantifier alternations, yielding#N#O(|G|^O(|G|^5)) time complexity, improving the previously known reduction,#N#which yielded O(|G|^O(|G|^7)) time complexity. These algorithms can be iterated#N#to approximate the metrics using binary search.

Connected through hyperedge HE-1 (induced by source paper "modal characterisations of behavioural pseudometrics"):
  with members:
    - "probabilistic bisimulation naturally on distributions"
    - "when equivalence and bisimulation join forces in probabilistic automata"
    - "bisimulations for nondeterministic labeled markov processes"
    - "approximating a behavioural pseudometric without discount for probabilistic systems"
    - "game refinement relations and metrics"

Connected through hyperedge HE-2 (induced by source paper "when equivalence and bisimulation join forces in probabilistic automata"):
  with members:
    - "deciding probabilistic automata weak bisimulation in polynomial time"
    - "on the complexity of the equivalence problem for probabilistic automata"
    - "approximating a behavioural pseudometric without discount for probabilistic systems"

Connected through hyperedge HE-3 (induced by source paper "weighted modal transition systems"):
  with members:
    - "distances for weighted transition systems games and properties"
    - "expressiveness and closure properties for quantitative languages"
    - "game refinement relations and metrics"

Connected through hyperedge HE-4 (induced by source paper "computing distances between probabilistic automata"):
  with members:
    - "metrics for finite markov decision processes"
    - "metrics for markov decision processes with infinite state spaces"
    - "approximating a behavioural pseudometric without discount for probabilistic systems"
    - "flow faster efficient decision algorithms for probabilistic simulations"

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)