human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "certified reinforcement learning with logic guidance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cautious reinforcement learning with logical constraints")
  Member "an investigation of model free planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "offworld gym open access physical robotics environment for real world reinforcement learning benchmark and research")
  Member "logically correct reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "verifiably safe off model reinforcement learning")
  Member "reinforcement learning with temporal logic rewards":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")
  Member "verification of markov decision processes using learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")
  Member "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "point based methods for model checking in partially observable markov decision processes")
  Member "probably approximately correct mdp learning and control with temporal logic constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")
  Member "automata with generalized rabin pairs for probabilistic model checking and ltl synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "near linear time algorithms for streett objectives in graphs and mdps")

Question: Please tell me which class the source paper 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)