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: "verification of markov decision processes using learning algorithms"
  Abstract: We present a general framework for applying machine-learning algorithms to the verification of Markov decision processes (MDPs). The primary goal of these techniques is to improve performance by avoiding an exhaustive exploration of the state space. Our framework focuses on probabilistic reachability, which is a core property for verification, and is illustrated through two distinct instantiations. The first assumes that full knowledge of the MDP is available, and performs a heuristic-driven partial exploration of the model, yielding precise lower and upper bounds on the required probability. The second tackles the case where we may only sample the MDP, and yields probabilistic guarantees, again in terms of both the lower and upper bounds, which provides efficient stopping criteria for the approximation. The latter is the first extension of statistical model-checking for unbounded properties in MDPs. In contrast with other related approaches, we do not restrict our attention to time-bounded (finite-horizon) or discounted properties, nor assume any particular properties of the MDP. We also show how our techniques extend to LTL objectives. We present experimental results showing the performance of our framework on several examples.

Connected through hyperedge HE-1 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives"):
  with members:
    - "temporal logic guided safe reinforcement learning using control barrier functions"
    - "transfer of temporal logic formulas in reinforcement learning"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "reinforcement learning with temporal logic rewards"
    - "on line monitoring for temporal logic robustness"
    - "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-2 (induced by source paper "symblicit exploration and elimination for probabilistic model checking"):
  with members:
    - "optimistic value iteration"
    - "incremental verification of parametric and reconfigurable markov chains"
    - "sound value iteration"
    - "accelerated model checking of parametric markov chains"
    - "continuous time markov decisions based on partial exploration"
    - "a storm is coming a modern probabilistic model checker"
    - "explicit model checking of very large mdp using partitioning and secondary storage"

Connected through hyperedge HE-3 (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-4 (induced by source paper "control synthesis from linear temporal logic specifications using model free reinforcement learning"):
  with members:
    - "logically constrained reinforcement learning"
    - "omega regular objectives in model free reinforcement learning"
    - "foundations for restraining bolts reinforcement learning with ltlf ldlf restraining specifications"
    - "a policy search method for temporal logic specified reinforcement learning tasks"
    - "probabilistic motion planning under temporal tasks and soft constraints"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "reinforcement learning with temporal logic rewards"
    - "correct by synthesis reinforcement learning with temporal logic constraints"

Connected through hyperedge HE-5 (induced by source paper "verifiably safe off model reinforcement learning"):
  with members:
    - "logically correct reinforcement learning"
    - "verifying controllers against adversarial examples with bayesian optimization"
    - "shielded decision making in mdps"
    - "falsification of cyber physical systems using deep reinforcement learning"
    - "a complete uniform substitution calculus for differential dynamic logic"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "constrained policy optimization"

Connected through hyperedge HE-6 (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"
    - "percentile queries in multi dimensional markov decision processes"
    - "robust multidimensional mean payoff games are undecidable"
    - "trading performance for stability in markov decision processes"
    - "pareto curves for probabilistic model checking"

Connected through hyperedge HE-7 (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"
    - "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"
    - "pareto curves for probabilistic model checking"

Connected through hyperedge HE-8 (induced by source paper "stopping criteria for value and strategy iteration on concurrent stochastic reachability games"):
  with members:
    - "sound value iteration"
    - "a storm is coming a modern probabilistic model checker"
    - "strategy improvement for concurrent reachability and safety games"
    - "recursive concurrent stochastic games"
    - "termination criteria for solving concurrent safety and reachability games"

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)