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 "reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees"):
  Abstract: Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics, and biology. In this paper we present a model-free RL algorithm to synthesize control policies that maximize the probability of satisfying high-level control objectives given as Linear Temporal Logic (LTL) formulas. Uncertainty is considered in the workspace properties, the structure of the workspace, and the agent actions, giving rise to a Probabilistically-Labeled Markov Decision Process (PL-MDP) with unknown graph structure and stochastic behaviour, which is even more general case than a fully unknown MDP. We first translate the LTL specification into a Limit Deterministic Buchi Automaton (LDBA), which is then used in an on-the-fly product with the PL-MDP. Thereafter, we define a synchronous reward function based on the acceptance condition of the LDBA. Finally, we show that the RL algorithm delivers a policy that maximizes the satisfaction probability asymptotically. We provide experimental results that showcase the efficiency of the proposed method.

Includes members:
  Member "verifiably safe off model reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cautious reinforcement learning with logical constraints")
  Member "logically constrained reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cautious reinforcement learning with logical constraints")
  Member "logically constrained neural fitted q iteration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cautious reinforcement learning with logical constraints")
  Member "omega regular objectives in model free reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "policy synthesis for factored mdps with graph temporal logic specifications")
  Member "probabilistic motion planning under temporal tasks and soft constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "socially intelligent task and motion planning for human robot interaction")
  Member "verification of markov decision processes using learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")

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)