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 "control synthesis from linear temporal logic specifications using model free reinforcement learning"):
  Abstract: We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that can be modeled as a Markov Decision Process (MDP). Specifically, we learn a policy that maximizes the probability of satisfying the LTL formula without learning the transition probabilities. We introduce a novel rewarding and path-dependent discounting mechanism based on the LTL formula such that (i) an optimal policy maximizing the total discounted reward effectively maximizes the probabilities of satisfying LTL objectives, and (ii) a model-free RL algorithm using these rewards and discount factors is guaranteed to converge to such policy. Finally, we illustrate the applicability of our RL-based synthesis approach on two motion planning case studies.

Includes members:
  Member "logically constrained reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "policy synthesis for factored mdps with graph temporal logic specifications")
  Member "foundations for restraining bolts reinforcement learning with ltlf ldlf restraining specifications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning non markovian reward models in mdps")
  Member "a policy search method for temporal logic specified reinforcement learning tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prescribed performance control guided policy improvement for satisfying signal temporal logic tasks")
  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 "q learning for robust satisfaction of signal temporal logic specifications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")
  Member "reinforcement learning with temporal logic rewards":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")
  Member "correct by synthesis reinforcement learning with temporal logic constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "certified reinforcement learning with logic guidance")

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.RO(Robotics)