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 "tractable reinforcement learning of signal temporal logic objectives"):
  Abstract: Signal temporal logic (STL) is an expressive language to specify time-bound real-world robotic tasks and safety specifications. Recently, there has been an interest in learning optimal policies to satisfy STL specifications via reinforcement learning (RL). Learning to satisfy STL specifications often needs a sufficient length of state history to compute reward and the next action. The need for history results in exponential state-space growth for the learning problem. Thus the learning problem becomes computationally intractable for most real-world applications. In this paper, we propose a compact means to capture state history in a new augmented state-space representation. An approximation to the objective (maximizing probability of satisfaction) is proposed and solved for in the new augmented state-space. We show the performance bound of the approximate solution and compare it with the solution of an existing technique via simulations.

Includes members:
  Member "temporal logic guided safe reinforcement learning using control barrier functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty")
  Member "transfer of temporal logic formulas in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences")
  Member "q learning for robust satisfaction of signal temporal logic specifications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "temporal logic guided safe reinforcement learning using control barrier functions")
  Member "reinforcement learning with temporal logic rewards":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certified reinforcement learning with logic guidance")
  Member "on line monitoring for temporal logic robustness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "f1 10 an open source autonomous cyber physical platform")
  Member "verification of markov decision processes using learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "symblicit exploration and elimination for probabilistic model checking")
  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 "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.LG(Machine Learning)