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: "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
  Abstract: We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a product MDP that incorporates a deterministic Rabin automaton generated from the desired LTL property. The reward function of the product MDP is defined from the acceptance condition of the Rabin automaton. This construction allows us to apply techniques from learning theory to the problem of synthesis for LTL specifications even when the transition probabilities are not known a priori. We prove that our method is guaranteed to find a controller that satisfies the LTL property with probability one if such a policy exists, and we suggest empirically with a case study in traffic control that our method produces reasonable control strategies even when the LTL property cannot be satisfied with probability one.

Connected through hyperedge HE-1 (induced by source paper "point based methods for model checking in partially observable markov decision processes"):
  with members:
    - "verification of uncertain pomdps using barrier certificates"
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "a storm is coming a modern probabilistic model checker"
    - "inverse reward design"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "what is decidable about partially observable markov decision processes with omega regular objectives"
    - "a theory of goal oriented mdps with dead ends"
    - "heuristic search value iteration for pomdps"

Connected through hyperedge HE-2 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives"):
  with members:
    - "temporal logic guided safe reinforcement learning using control barrier functions"
    - "transfer of temporal logic formulas in reinforcement learning"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "reinforcement learning with temporal logic rewards"
    - "on line monitoring for temporal logic robustness"
    - "verification of markov decision processes using learning algorithms"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Connected through hyperedge HE-3 (induced by source paper "certified reinforcement learning with logic guidance"):
  with members:
    - "the probabilistic model checker storm"
    - "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "from game theoretic multi agent log linear learning to reinforcement learning"
    - "observe and look further achieving consistent performance on atari"

Connected through hyperedge HE-4 (induced by source paper "temporal logic guided safe reinforcement learning using control barrier functions"):
  with members:
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "automata guided reinforcement learning with demonstrations"
    - "safe exploration in continuous action spaces"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "reinforcement learning for ltlf ldlf goals"

Connected through hyperedge HE-5 (induced by source paper "design space of behaviour planning for autonomous driving"):
  with members:
    - "learning on road visual control for self driving vehicles with auxiliary tasks"
    - "world models"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "environment independent task specifications via gltl"
    - "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"
    - "learning sampling distributions for robot motion planning"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"

Connected through hyperedge HE-6 (induced by source paper "control synthesis from linear temporal logic specifications using model free reinforcement learning"):
  with members:
    - "logically constrained reinforcement learning"
    - "omega regular objectives in model free 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"

Connected through hyperedge HE-7 (induced by source paper "planning with state abstractions for non markovian task specifications"):
  with members:
    - "evaluating compositionality in sentence embeddings"
    - "accurately and efficiently interpreting human robot instructions of varying granularities"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "six challenges for neural machine translation"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "constructing abstraction hierarchies using a skill symbol loop"

Connected through hyperedge HE-8 (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"
    - "omega regular objectives in model free reinforcement learning"
    - "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"

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.SY(Systems and Control)