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: "game refinement relations and metrics"
  Abstract: We consider two-player games played over finite state spaces for an infinite number of rounds. At each state, the players simultaneously choose moves; the moves determine a successor state. It is often advantageous for players to choose probability distributions over moves, rather than single moves. Given a goal, for example, reach a target state, the question of winning is thus a probabilistic one: what is the maximal probability of winning from a given state? On these game structures, two fundamental notions are those of equivalences and metrics. Given a set of winning conditions, two states are equivalent if the players can win the same games with the same probability from both states. Metrics provide a bound on the difference in the probabilities of winning across states, capturing a quantitative notion of state similarity. We introduce equivalences and metrics for two-player game structures, and we show that they characterize the difference in probability of winning games whose goals are expressed in the quantitative mu-calculus. The quantitative mu-calculus can express a large set of goals, including reachability, safety, and omega-regular properties. Thus, we claim that our relations and metrics provide the canonical extensions to games, of the classical notion of bisimulation for transition systems. We develop our results both for equivalences and metrics, which generalize bisimulation, and for asymmetrical versions, which generalize simulation.

Connected through hyperedge HE-1 (induced by source paper "characterising probabilistic alternating simulation for concurrent games"):
  with members:
    - "on probabilistic alternating simulations"

Connected through hyperedge HE-2 (induced by source paper "logical characterization of trace metrics"):
  with members:
    - "logical characterization of bisimulation metrics"
    - "revisiting trace and testing equivalences for nondeterministic and probabilistic processes"
    - "logical metric and algorithmic characterisations of probabilistic bisimulation"

Connected through hyperedge HE-3 (induced by source paper "logical characterization of bisimulation metrics"):
  with members:
    - "divide and congruence ii from decomposition of modal formulas to preservation of delay and weak bisimilarity"
    - "modal characterisations of behavioural pseudometrics"
    - "topologies of stochastic markov models computational aspects"
    - "probabilistic bisimulation naturally on distributions"
    - "fixed point characterization of compositionality properties of probabilistic processes combinators"
    - "when equivalence and bisimulation join forces in probabilistic automata"
    - "continuous markovian logics axiomatization and quantified metatheory"
    - "logical metric and algorithmic characterisations of probabilistic bisimulation"

Connected through hyperedge HE-4 (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"
    - "algorithms for game metrics"
    - "approximating a behavioural pseudometric without discount for probabilistic systems"

Connected through hyperedge HE-5 (induced by source paper "the spectrum of strong behavioral equivalences for nondeterministic and probabilistic processes"):
  with members:
    - "revisiting trace and testing equivalences for nondeterministic and probabilistic processes"
    - "bisimulations meet pctl equivalences for probabilistic automata"
    - "computing distances between probabilistic automata"
    - "characterising testing preorders for finite probabilistic processes"

Connected through hyperedge HE-6 (induced by source paper "weighted modal transition systems"):
  with members:
    - "distances for weighted transition systems games and properties"
    - "algorithms for game metrics"
    - "expressiveness and closure properties for quantitative languages"

Connected through hyperedge HE-7 (induced by source paper "interface simulation distances"):
  with members:
    - "generalized mean payoff and energy games"

Connected through hyperedge HE-8 (induced by source paper "an algorithm for probabilistic alternating simulation"):
  with members:
    - "on probabilistic alternating simulations"
    - "non deterministic kleene coalgebras"
    - "flow faster efficient decision algorithms for probabilistic simulations"
    - "first order and temporal logics for nested words"

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.LO(Logic in Computer Science)