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 constrained reinforcement learning"
  Abstract: We present the first model-free Reinforcement Learning (RL) algorithm to synthesise policies for an unknown Markov Decision Process (MDP), such that a linear time property is satisfied. The given temporal property is converted into a Limit Deterministic Buchi Automaton (LDBA) and a robust reward function is defined over the state-action pairs of the MDP according to the resulting LDBA. With this reward function, the policy synthesis procedure is "constrained" by the given specification. These constraints guide the MDP exploration so as to minimize the solution time by only considering the portion of the MDP that is relevant to satisfaction of the LTL property. This improves performance and scalability of the proposed method by avoiding an exhaustive update over the whole state space while the efficiency of standard methods such as dynamic programming is hindered by excessive memory requirements, caused by the need to store a full-model in memory. Additionally, we show that the RL procedure sets up a local value iteration method to efficiently calculate the maximum probability of satisfying the given property, at any given state of the MDP. We prove that our algorithm is guaranteed to find a policy whose traces probabilistically satisfy the LTL property if such a policy exists, and additionally we show that our method produces reasonable control policies even when the LTL property cannot be satisfied. 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 "cautious reinforcement learning with logical constraints"):
  with members:
    - "certified reinforcement learning with logic guidance"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees"
    - "modular deep reinforcement learning with temporal logic specifications"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained neural fitted q iteration"
    - "shielded decision making in mdps"
    - "safe reinforcement learning via shielding"

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"
    - "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"
    - "shielded decision making in mdps"

Connected through hyperedge HE-3 (induced by source paper "control synthesis from linear temporal logic specifications using model free reinforcement learning"):
  with members:
    - "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"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"):
  with members:
    - "learning concise models from long execution traces"
    - "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"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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 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"
    - "probably approximately correct mdp learning and control with temporal logic constraints"
    - "pareto curves for probabilistic model checking"

Connected through hyperedge HE-6 (induced by source paper "shielded decision making in mdps"):
  with members:
    - "maximizing the conditional expected reward for reaching the goal"
    - "a storm is coming a modern probabilistic model checker"
    - "safe reinforcement learning via shielding"
    - "a berkeley view of systems challenges for ai"
    - "concrete problems in ai safety"
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "shield synthesis runtime enforcement for reactive systems"
    - "safety constrained reinforcement learning for mdps"

Connected through hyperedge HE-7 (induced by source paper "logically constrained neural fitted q iteration"):
  with members:
    - "certified reinforcement learning with logic guidance"
    - "verifiably safe off model reinforcement learning"
    - "omega regular objectives in model free reinforcement learning"
    - "safe reinforcement learning via shielding"
    - "asynchronous methods for deep reinforcement learning"
    - "modular multitask reinforcement learning with policy sketches"
    - "verification of markov decision processes using learning algorithms"
    - "safe exploration in markov decision processes"

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)