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: "logically correct reinforcement learning"
  Abstract: We propose a novel Reinforcement Learning (RL) algorithm to synthesize policies for a Markov Decision Process (MDP), such that a linear time property is satisfied. We convert the property into a Limit Deterministic Buchi Automaton (LDBA), then construct a product MDP between the automaton and the original MDP. A reward function is then assigned to the states of the product automaton, according to accepting conditions of the LDBA. With this reward function, RL synthesizes a policy that satisfies the property: as such, the policy synthesis procedure is "constrained" by the given specification. Additionally, we show that the RL procedure sets up an online value iteration method to calculate the maximum probability of satisfying the given property, at any given state of the MDP - a convergence proof for the procedure is provided. Finally, the performance of the algorithm is evaluated via a set of numerical examples. We observe an improvement of one order of magnitude in the number of iterations required for the synthesis compared to existing approaches.

Connected through hyperedge HE-1 (induced by source paper "verifiably safe off model reinforcement learning"):
  with members:
    - "verifying controllers against adversarial examples with bayesian optimization"
    - "shielded decision making in mdps"
    - "falsification of cyber physical systems using deep reinforcement learning"
    - "a complete uniform substitution calculus for differential dynamic logic"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-2 (induced by source paper "safe reinforcement learning via probabilistic shields"):
  with members:
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "learning robotic manipulation through visual planning and acting"
    - "smartplan a task planner for smart factories"
    - "a lyapunov based approach to safe reinforcement learning"
    - "a storm is coming a modern probabilistic model checker"
    - "safe reinforcement learning via shielding"
    - "concrete problems in ai safety"
    - "correct by synthesis reinforcement learning with temporal logic constraints"

Connected through hyperedge HE-3 (induced by source paper "omega regular objectives in model free reinforcement learning"):
  with members:
    - "certified reinforcement learning with logic guidance"
    - "an investigation of model free planning"
    - "reinforcement learning with temporal logic rewards"
    - "verification of markov decision processes using learning algorithms"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "probably approximately correct mdp learning and control with temporal logic constraints"
    - "automata with generalized rabin pairs for probabilistic model checking and ltl synthesis"
    - "lazy probabilistic model checking without determinisation"

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.LG(Machine Learning)