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: "flow faster efficient decision algorithms for probabilistic simulations"
  Abstract: Strong and weak simulation relations have been proposed for Markov chains,#N#while strong simulation and strong probabilistic simulation relations have been#N#proposed for probabilistic automata. However, decision algorithms for strong#N#and weak simulation over Markov chains, and for strong simulation over#N#probabilistic automata are not efficient, which makes it as yet unclear whether#N#they can be used as effectively as their non-probabilistic counterparts. This#N#paper presents drastically improved algorithms to decide whether some#N#(discrete- or continuous-time) Markov chain strongly or weakly simulates#N#another, or whether a probabilistic automaton strongly simulates another. The#N#key innovation is the use of parametric maximum flow techniques to amortize#N#computations. We also present a novel algorithm for deciding strong#N#probabilistic simulation preorders on probabilistic automata, which has#N#polynomial complexity via a reduction to an LP problem. When extending the#N#algorithms for probabilistic automata to their continuous-time counterpart, we#N#retain the same complexity for both strong and strong probabilistic#N#simulations.

Connected through hyperedge HE-1 (induced by source paper "bisimulations and logical characterizations on continuous time markov decision processes"):
  with members:
    - "bisimulations meet pctl equivalences for probabilistic automata"
    - "computing distances between probabilistic automata"
    - "efficient approximation of optimal control for markov games"
    - "finite optimal control for time bounded reachability in ctmdps and continuous time markov games"

Connected through hyperedge HE-2 (induced by source paper "logical metric and algorithmic characterisations of probabilistic bisimulation"):
  with members:
    - "metrics for finite markov decision processes"
    - "metrics for markov decision processes with infinite state spaces"
    - "characterising probabilistic processes logically"
    - "approximating a behavioural pseudometric without discount for probabilistic systems"
    - "characterising testing preorders for finite probabilistic processes"

Connected through hyperedge HE-3 (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"
    - "algorithms for game metrics"
    - "approximating a behavioural pseudometric without discount for probabilistic systems"

Connected through hyperedge HE-4 (induced by source paper "an algorithm for probabilistic alternating simulation"):
  with members:
    - "on probabilistic alternating simulations"
    - "non deterministic kleene coalgebras"
    - "first order and temporal logics for nested words"
    - "game refinement relations and metrics"

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)