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: "correct by synthesis reinforcement learning with temporal logic constraints"
  Abstract: We consider a problem on the synthesis of reactive controllers that optimize some a priori unknown performance criterion while interacting with an uncontrolled environment such that the system satisfies a given temporal logic specification. We decouple the problem into two subproblems. First, we extract a (maximally) permissive strategy for the system, which encodes multiple (possibly all) ways in which the system can react to the adversarial environment and satisfy the specifications. Then, we quantify the a priori unknown performance criterion as a (still unknown) reward function and compute an optimal strategy for the system within the operating envelope allowed by the permissive strategy by using the so-called maximin-Q learning algorithm. We establish both correctness (with respect to the temporal logic specifications) and optimality (with respect to the a priori unknown performance criterion) of this two-step technique for a fragment of temporal logic specifications. For specifications beyond this fragment, correctness can still be preserved, but the learned strategy may be sub-optimal. We present an algorithm to the overall problem, and demonstrate its use and computational requirements on a set of robot motion planning examples.

Connected through hyperedge HE-1 (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-2 (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"
    - "verification of markov decision processes using learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "safe reinforcement learning on autonomous vehicles"):
  with members:
    - "selective experience replay for lifelong learning"
    - "imitating driver behavior with generative adversarial networks"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "safe reinforcement learning via shielding"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "safe exploration in markov decision processes"
    - "risk sensitive reinforcement learning applied to control under constraints"

Connected through hyperedge HE-4 (induced by source paper "reconnaissance and planning algorithm for constrained mdp"):
  with members:
    - "reinforcement learning with probabilistic guarantees for autonomous driving"
    - "lyapunov based safe policy optimization for continuous control"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "a lyapunov based approach to safe reinforcement learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "constrained policy optimization"
    - "risk sensitive reinforcement learning applied to control under constraints"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning driven test generation for android gui applications using formal specifications"):
  with members:
    - "certified reinforcement learning with logic guidance"
    - "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"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "shielded decision making in mdps"):
  with members:
    - "logically constrained reinforcement learning"
    - "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"
    - "shield synthesis runtime enforcement for reactive systems"
    - "safety constrained reinforcement learning for mdps"

Connected through hyperedge HE-7 (induced by source paper "logically constrained reinforcement learning"):
  with members:
    - "value iteration for long run average reward in markov decision processes"
    - "safe reinforcement learning via shielding"
    - "asynchronous methods for deep reinforcement learning"
    - "reinforcement learning with temporal logic rewards"
    - "safety constrained reinforcement learning for mdps"
    - "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"

Connected through hyperedge HE-8 (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"
    - "logically correct reinforcement learning"
    - "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"

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.LO(Logic in Computer Science)