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: "episodic learning with control lyapunov functions for uncertain robotic systems"
  Abstract: Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller.

Connected through hyperedge HE-1 (induced by source paper "beyond no regret competitive control via online optimization with memory"):
  with members:
    - "chasing convex bodies optimally"
    - "robust regression for safe exploration in control"
    - "competitive online optimization under inventory constraints"
    - "online control with adversarial disturbances"
    - "beyond online balanced descent an optimal algorithm for smoothed online optimization"
    - "neural swarm decentralized close proximity multirotor controlusing learned interactions"
    - "online inventory management with application to energy procurement in data centers"
    - "online optimization with predictions and non convex losses"

Connected through hyperedge HE-2 (induced by source paper "learning stable deep dynamics models"):
  with members:
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "a lyapunov based approach to safe reinforcement learning"
    - "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-3 (induced by source paper "robust regression for safe exploration in control"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "control regularization for reduced variance reinforcement learning"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "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-4 (induced by source paper "probabilistic safety constraints for learned high relative degree system dynamics"):
  with members:
    - "control barrier functions theory and applications"
    - "robust regression for safe exploration in control"
    - "self triggered control for safety critical systems using control barrier functions"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "meta learning priors for efficient online bayesian regression"
    - "safe learning of quadrotor dynamics using barrier certificates"
    - "least squares temporal difference learning for the linear quadratic regulator"

Connected through hyperedge HE-5 (induced by source paper "a control lyapunov perspective on episodic learning via projection to state stability"):
  with members:
    - "stagewise safe bayesian optimization with gaussian processes"
    - "neural lander stable drone landing control using learned dynamics"
    - "differentiable mpc for end to end planning and control"
    - "spectral normalization for generative adversarial networks"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "torque saturation in bipedal robotic walking through control lyapunov function based quadratic programs"

Connected through hyperedge HE-6 (induced by source paper "neural swarm decentralized close proximity multirotor controlusing learned interactions"):
  with members:
    - "robust regression for safe exploration in control"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "control regularization for reduced variance reinforcement learning"
    - "neural lander stable drone landing control using learned dynamics"
    - "residual reinforcement learning for robot control"
    - "deep sets"
    - "learn fast forget slow safe predictive learning control for systems with unknown and changing dynamics performing repetitive tasks"
    - "smooth imitation learning for online sequence prediction"

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.RO(Robotics)