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: "a lyapunov based approach to safe reinforcement learning"
  Abstract: In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints. In particular, besides optimizing performance it is crucial to guarantee the safety of an agent during training as well as deployment (e.g. a robot should avoid taking actions - exploratory or not - which irrevocably harm its hardware). To incorporate safety in RL, we derive algorithms under the framework of constrained Markov decision problems (CMDPs), an extension of the standard Markov decision problems (MDPs) augmented with constraints on expected cumulative costs. Our approach hinges on a novel \emph{Lyapunov} method. We define and present a method for constructing Lyapunov functions, which provide an effective way to guarantee the global safety of a behavior policy during training via a set of local, linear constraints. Leveraging these theoretical underpinnings, we show how to use the Lyapunov approach to systematically transform dynamic programming (DP) and RL algorithms into their safe counterparts. To illustrate their effectiveness, we evaluate these algorithms in several CMDP planning and decision-making tasks on a safety benchmark domain. Our results show that our proposed method significantly outperforms existing baselines in balancing constraint satisfaction and performance.

Connected through hyperedge HE-1 (induced by source paper "constrained upper confidence reinforcement learning"):
  with members:
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "learning based model predictive control for safe exploration"
    - "ai safety gridworlds"
    - "budget constrained multi armed bandits with multiple plays"
    - "constrained policy optimization"
    - "fairness in learning classic and contextual bandits"
    - "concrete problems in ai safety"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "conservative exploration in reinforcement learning"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"
    - "constrained policy optimization"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "bayesian incentive compatible bandit exploration"
    - "counterfactual risk minimization learning from logged bandit feedback"

Connected through hyperedge HE-3 (induced by source paper "learning stable deep dynamics models"):
  with members:
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "prediction and control with temporal segment models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous deep q learning with model based acceleration"
    - "input convex neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "challenges of real world reinforcement learning"):
  with members:
    - "value constrained model free continuous control"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reward constrained policy optimization"

Connected through hyperedge HE-5 (induced by source paper "convergent policy optimization for safe reinforcement learning"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "provably efficient q learning with low switching cost"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "neural temporal difference learning converges to global optima"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "stochastic successive convex approximation for non convex constrained stochastic optimization"
    - "learning safe policies with expert guidance"
    - "communication efficient distributed reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "caql continuous action q learning"):
  with members:
    - "equivalent and approximate transformations of deep neural networks"
    - "challenges of real world reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-7 (induced by source paper "episodic learning with control lyapunov functions for uncertain robotic systems"):
  with members:
    - "stable gaussian process based tracking control of euler lagrange systems"
    - "a control lyapunov perspective on episodic learning via projection to state stability"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "neural lander stable drone landing control using learned dynamics"
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "sim to real learning agile locomotion for quadruped robots"
    - "accelerating imitation learning with predictive models"
    - "safe learning of quadrotor dynamics using barrier certificates"

Connected through hyperedge HE-8 (induced by source paper "don t forget your teacher a corrective reinforcement learning framework"):
  with members:
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "programmatically interpretable reinforcement learning"
    - "kickstarting deep reinforcement learning"
    - "ai safety gridworlds"
    - "divide and conquer reinforcement learning"
    - "constrained policy optimization"
    - "a deep q network for the beer game a deep reinforcement learning algorithm to solve inventory optimization problems"
    - "distral robust multitask reinforcement learning"

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)