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: "omega regular objectives in model free reinforcement learning"
  Abstract: We provide the first solution for model-free reinforcement learning of {\omega}-regular objectives for Markov decision processes (MDPs). We present a constructive reduction from the almost-sure satisfaction of {\omega}-regular objectives to an almost- sure reachability problem and extend this technique to learning how to control an unknown model so that the chance of satisfying the objective is maximized. A key feature of our technique is the compilation of {\omega}-regular properties into limit- deterministic Buechi automata instead of the traditional Rabin automata; this choice sidesteps difficulties that have marred previous proposals. Our approach allows us to apply model-free, off-the-shelf reinforcement learning algorithms to compute optimal strategies from the observations of the MDP. We present an experimental evaluation of our technique on benchmark learning problems.

Connected through hyperedge HE-1 (induced by source paper "policy synthesis for factored mdps with graph temporal logic specifications"):
  with members:
    - "graph temporal logic inference for classification and identification"
    - "reward based deception with cognitive bias"
    - "motion and cooperative transportation planning for multi agent systems under temporal logic formulas"
    - "environment independent task specifications via gltl"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "robotic swarm control from spatio temporal specifications"
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-2 (induced by source paper "reward shaping for reinforcement learning with omega regular objectives"):
  with members:
    - "good for mdps automata for probabilistic analysis and reinforcement learning"
    - "control synthesis from linear temporal logic specifications using model free reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "control synthesis from linear temporal logic specifications using model free reinforcement learning"):
  with members:
    - "logically constrained 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"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"):
  with members:
    - "learning concise models from long execution traces"
    - "logically constrained reinforcement learning"
    - "exact finite state machine identification from scenarios and temporal properties"
    - "logically constrained neural fitted q iteration"
    - "foundations for restraining bolts reinforcement learning with ltlf ldlf restraining specifications"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "modular multitask reinforcement learning with policy sketches"
    - "adam a method for stochastic optimization"

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"
    - "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"
    - "pareto curves for probabilistic model checking"

Connected through hyperedge HE-6 (induced by source paper "good for mdps automata for probabilistic analysis and reinforcement learning"):
  with members:
    - "proximal policy optimization algorithms"
    - "adam a method for stochastic optimization"
    - "determinising parity automata"
    - "state of buchi complementation"
    - "lazy probabilistic model checking without determinisation"

Connected through hyperedge HE-7 (induced by source paper "pac statistical model checking for markov decision processes and stochastic games"):
  with members:
    - "certified reinforcement learning with logic guidance"
    - "faster statistical model checking for unbounded temporal properties"
    - "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-8 (induced by source paper "good for mdps automata"):
  with members:
    - "state of buchi complementation"
    - "lazy probabilistic model checking without determinisation"
    - "ltl to b uchi automata translation fast and more deterministic"

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)