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: "probably approximately correct mdp learning and control with temporal logic constraints"
  Abstract: We consider synthesis of control policies that maximize the probability of satisfying given temporal logic specifications in unknown, stochastic environments. We model the interaction between the system and its environment as a Markov decision process (MDP) with initially unknown transition probabilities. The solution we develop builds on the so-called model-based probably approximately correct Markov decision process (PAC-MDP) methodology. The algorithm attains an $\varepsilon$-approximately optimal policy with probability $1-\delta$ using samples (i.e. observations), time and space that grow polynomially with the size of the MDP, the size of the automaton expressing the temporal logic specification, $\frac{1}{\varepsilon}$, $\frac{1}{\delta}$ and a finite time horizon. In this approach, the system maintains a model of the initially unknown MDP, and constructs a product MDP based on its learned model and the specification automaton that expresses the temporal logic constraints. During execution, the policy is iteratively updated using observation of the transitions taken by the system. The iteration terminates in finitely many steps. With high probability, the resulting policy is such that, for any state, the difference between the probability of satisfying the specification under this policy and the optimal one is within a predefined bound.

Connected through hyperedge HE-1 (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"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"

Connected through hyperedge HE-2 (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-3 (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-4 (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-5 (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"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees"):
  with members:
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "logically constrained neural fitted q iteration"
    - "omega regular objectives in model free reinforcement learning"
    - "probabilistic motion planning under temporal tasks and soft constraints"
    - "verification of markov decision processes using learning algorithms"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "pareto curves for probabilistic model checking"

Connected through hyperedge HE-7 (induced by source paper "modular deep reinforcement learning with temporal logic specifications"):
  with members:
    - "logically constrained neural fitted q iteration"
    - "foundations for restraining bolts reinforcement learning with ltlf ldlf restraining specifications"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "strategic attentive writer for learning macro actions"
    - "modular multitask reinforcement learning with policy sketches"
    - "quantitative approximation of the probability distribution of a markov process by formal abstractions"
    - "continuous control with deep reinforcement learning"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"

Connected through hyperedge HE-8 (induced by source paper "transfer of temporal logic formulas in reinforcement learning"):
  with members:
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "learning linear temporal properties"
    - "learning task specifications from demonstrations"
    - "safe reinforcement learning via shielding"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "reinforcement learning with temporal logic rewards"

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)