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: "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
  Abstract: This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique. We use the learned model in combination with control barrier certificates that constrain policies (feedback controllers) in order to maintain safety, which refers to avoiding particular undesirable regions of the state space. Under certain conditions, recovery of safety in the sense of Lyapunov stability after violations of safety due to the nonstationarity is guaranteed. In addition, we reformulate an action-value function approximation to make any kernel-based nonlinear function estimation method applicable to our adaptive learning framework. Lastly, solutions to the barrier-certified policy optimization are guaranteed to be globally optimal, ensuring the greedy policy improvement under mild conditions. The resulting framework is validated via simulations of a quadrotor, which has previously been used under  stationarity  assumptions in the safe learnings literature, and is then tested on a real robot, the brushbot, whose dynamics is unknown, highly complex, and nonstationary.

Connected through hyperedge HE-1 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty"):
  with members:
    - "control barrier functions theory and applications"
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "algorithms for verifying deep neural networks"
    - "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"
    - "temporal logic guided safe reinforcement learning using control barrier functions"
    - "worst cases policy gradients"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "sampling based motion planning via control barrier functions"

Connected through hyperedge HE-2 (induced by source paper "temporal logic guided safe reinforcement learning using control barrier functions"):
  with members:
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "automata guided reinforcement learning with demonstrations"
    - "safe exploration in continuous action spaces"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "reinforcement learning for ltlf ldlf goals"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"

Connected through hyperedge HE-3 (induced by source paper "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"):
  with members:
    - "a lyapunov based approach to safe reinforcement learning"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "scalable synthesis of safety certificates from data with application to learning based control"
    - "safe reinforcement learning via shielding"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "safe exploration in markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "safe reinforcement learning via probabilistic shields"):
  with members:
    - "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"
    - "correct by synthesis reinforcement learning with temporal logic constraints"

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)