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: "pareto curves for probabilistic model checking"
  Abstract: Multi-objective probabilistic model checking provides a way to verify several, possibly conflicting, quantitative properties of a stochastic system. It has useful applications in controller synthesis and compositional probabilistic verification. However, existing methods are based on linear programming, which limits the scale of systems that can be analysed and makes verification of time-bounded properties very difficult. We present a novel approach that addresses both of these shortcomings, based on the generation of successive approximations of the Pareto curve for a multi-objective model checking problem. We illustrate dramatic improvements in efficiency on a large set of benchmarks and show how the ability to visualise Pareto curves significantly enhances the quality of results obtained from current probabilistic verification tools.

Connected through hyperedge HE-1 (induced by source paper "the probabilistic model checker storm"):
  with members:
    - "counterexample driven synthesis for probabilistic program sketches"
    - "safety analysis for vehicle guidance systems with dynamic fault trees"
    - "parameter synthesis for markov models"
    - "value iteration for long run average reward in markov decision processes"
    - "markov automata with multiple objectives"
    - "maximizing the conditional expected reward for reaching the goal"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"

Connected through hyperedge HE-2 (induced by source paper "certified reinforcement learning with logic guidance"):
  with members:
    - "the probabilistic model checker storm"
    - "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "from game theoretic multi agent log linear learning to reinforcement learning"
    - "observe and look further achieving consistent performance on atari"

Connected through hyperedge HE-3 (induced by source paper "simple strategies in multi objective mdps technical report"):
  with members:
    - "multi weighted markov decision processes with reachability objectives"
    - "markov automata with multiple objectives"
    - "threshold constraints with guarantees for parity objectives in markov decision processes"
    - "a storm is coming a modern probabilistic model checker"
    - "multi objective approaches to markov decision processes with uncertain transition parameters"
    - "variations on the stochastic shortest path problem"
    - "mixed integer quadratic programming is in np"
    - "percentile queries in multi dimensional markov decision processes"

Connected through hyperedge HE-4 (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"
    - "multigain a controller synthesis tool for mdps with multiple 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"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees"):
  with members:
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "logically constrained neural fitted q iteration"
    - "omega regular objectives in model free reinforcement learning"
    - "probabilistic motion planning under temporal tasks and soft constraints"
    - "verification of markov decision processes using learning algorithms"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Connected through hyperedge HE-6 (induced by source paper "ergodic mean payoff games for the analysis of attacks in crypto currencies"):
  with members:
    - "quantitative analysis of smart contracts"
    - "bitcoin s security model revisited"
    - "optimal selfish mining strategies in bitcoin"
    - "the complexity of ergodic mean payoff games"
    - "analysis of hashrate based double spending"
    - "the miner s dilemma"
    - "majority is not enough bitcoin mining is vulnerable"
    - "analysis of bitcoin pooled mining reward systems"

Connected through hyperedge HE-7 (induced by source paper "logically constrained reinforcement learning"):
  with members:
    - "value iteration for long run average reward in markov decision processes"
    - "safe reinforcement learning via shielding"
    - "asynchronous methods for deep reinforcement learning"
    - "reinforcement learning with temporal logic rewards"
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "safety constrained reinforcement learning for mdps"
    - "verification of markov decision processes using learning algorithms"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"

Connected through hyperedge HE-8 (induced by source paper "structured synthesis for probabilistic systems"):
  with members:
    - "shepherding hordes of markov chains"
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "high level counterexamples for probabilistic automata"
    - "multi objective model checking of markov decision processes"

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)