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: "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
  Abstract: Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) on-line learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework leverages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties. #R##N#Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous car-following with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process.

Connected through hyperedge HE-1 (induced by source paper "exploration exploitation in constrained mdps"):
  with members:
    - "conservative exploration in reinforcement learning"
    - "improved algorithms for conservative exploration in bandits"
    - "optimistic policy optimization with bandit feedback"
    - "constrained upper confidence reinforcement learning"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "provably efficient exploration in policy optimization"

Connected through hyperedge HE-2 (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"
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "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"
    - "sampling based motion planning via control barrier functions"

Connected through hyperedge HE-3 (induced by source paper "constrained upper confidence reinforcement learning"):
  with members:
    - "a lyapunov based approach to safe reinforcement learning"
    - "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-4 (induced by source paper "safe predictors for enforcing input output specifications"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "verifying aircraft collision avoidance neural networks through linear approximations of safe regions"
    - "art abstraction refinement guided training for provably correct neural networks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "towards proving the adversarial robustness of deep neural networks"
    - "optnet differentiable optimization as a layer in neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"

Connected through hyperedge HE-5 (induced by source paper "synthesis of control barrier functions using a supervised machine learning approach"):
  with members:
    - "autonomous navigation in unknown environments using sparse kernel based occupancy mapping"
    - "control barrier functions theory and applications"
    - "an optimal task allocation strategy for heterogeneous multi robot systems"
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "control of multi agent systems with finite time control barrier certificates and temporal logic"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-6 (induced by source paper "robust regression for safe exploration in control"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "control regularization for reduced variance reinforcement learning"
    - "stagewise safe bayesian optimization with gaussian processes"
    - "neural lander stable drone landing control using learned dynamics"
    - "spectral normalization for generative adversarial networks"
    - "measuring and regularizing networks in function space"
    - "a general safety framework for learning based control in uncertain robotic systems"

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"
    - "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"
    - "a lyapunov based approach to safe reinforcement learning"
    - "accelerating imitation learning with predictive models"
    - "safe learning of quadrotor dynamics using barrier certificates"

Connected through hyperedge HE-8 (induced by source paper "temporal logic guided safe reinforcement learning using control barrier functions"):
  with members:
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "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"

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)