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: "faster statistical model checking for unbounded temporal properties"
  Abstract: We present a new algorithm for the statistical model checking of Markov chains with respect to unbounded temporal properties, such as reachability and full linear temporal logic. The main idea is that we monitor each simulation run on the fly, in order to detect quickly if a bottom strongly connected component is entered with high probability, in which case the simulation run can be terminated early. As a result, our simulation runs are often much shorter than required by termination bounds that are computed a priori for a desired level of confidence and size of the state space. In comparison to previous algorithms for statistical model checking, for a given level of confidence, our method is not only faster in many cases but also requires less information about the system, namely, only the minimum transition probability that occurs in the Markov chain, thus enabling almost complete black-box verification. In addition, our method can be generalised to unbounded quantitative properties such as mean-payoff bounds.

Connected through hyperedge HE-1 (induced by source paper "pac statistical model checking for markov decision processes and stochastic games"):
  with members:
    - "certified reinforcement learning with logic guidance"
    - "omega regular objectives in model free reinforcement learning"
    - "smart sampling for lightweight verification of markov decision processes"
    - "probably approximately correct mdp learning and control with temporal logic constraints"
    - "scalable verification of markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "sound value iteration"):
  with members:
    - "a storm is coming a modern probabilistic model checker"
    - "verification of markov decision processes using learning algorithms"
    - "efficient computation of exact solutions for quantitative model checking"
    - "pareto curves for probabilistic model checking"
    - "topological value iteration algorithms"

Connected through hyperedge HE-3 (induced by source paper "a counting semantics for monitoring ltl specifications over finite traces"):
  with members:
    - "discounting in ltl"

Connected through hyperedge HE-4 (induced by source paper "multilevel monte carlo method for statistical model checking of hybrid systems"):
  with members:
    - "aggregation and control of populations of thermostatically controlled loads by formal abstractions"
    - "statistical model checking an overview"

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)