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: "certified reinforcement learning with logic guidance"
  Abstract: This paper proposes the first model-free Reinforcement Learning (RL) framework to synthesise policies for unknown, and continuous-state Markov Decision Processes (MDPs), such that a given linear temporal property is satisfied. We convert the given property into a Limit Deterministic Buchi Automaton (LDBA), namely a finite-state machine expressing the property. Exploiting the structure of the LDBA, we shape a synchronous reward function on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces that probabilistically satisfy the linear temporal property. This probability (certificate) is also calculated in parallel with policy learning when the state space of the MDP is finite: as such, the RL algorithm produces a policy that is certified with respect to the property. Under the assumption of finite state space, theoretical guarantees are provided on the convergence of the RL algorithm to an optimal policy, maximising the above probability. We also show that our method produces ''best available'' control policies when the logical property cannot be satisfied. In the general case of a continuous state space, we propose a neural network architecture for RL and we empirically show that the algorithm finds satisfying policies, if there exist such policies. The performance of the proposed framework is evaluated via a set of numerical examples and benchmarks, where we observe an improvement of one order of magnitude in the number of iterations required for the policy synthesis, compared to existing approaches whenever available.

Connected through hyperedge HE-1 (induced by source paper "cautious reinforcement learning with logical constraints"):
  with members:
    - "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 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 "reinforcement learning driven test generation for android gui applications using formal specifications"):
  with members:
    - "logically constrained neural fitted q iteration"
    - "mining android app usages for generating actionable gui based execution scenarios"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "automatically discovering reporting and reproducing android application crashes"
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "pac statistical model checking for markov decision processes and stochastic games"):
  with members:
    - "omega regular objectives in model free reinforcement learning"
    - "faster statistical model checking for unbounded temporal properties"
    - "smart sampling for lightweight verification of markov decision processes"
    - "probably approximately correct mdp learning and control with temporal logic constraints"
    - "scalable verification of markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "logically constrained neural fitted q iteration"):
  with members:
    - "verifiably safe off model reinforcement learning"
    - "logically constrained 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"

Connected through hyperedge HE-5 (induced by source paper "omega regular objectives in model free reinforcement learning"):
  with members:
    - "an investigation of model free planning"
    - "logically correct reinforcement learning"
    - "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)