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: "safe learning of quadrotor dynamics using barrier certificates"
  Abstract: To effectively control complex dynamical systems, accurate nonlinear models are typically needed. However, these models are not always known. In this paper, we present a data-driven approach based on Gaussian processes that learns models of quadrotors operating in partially unknown environments. What makes this challenging is that if the learning process is not carefully controlled, the system will go unstable, i.e., the quadcopter will crash. To this end, barrier certificates are employed for safe learning. The barrier certificates establish a non-conservative forward invariant safe region, in which high probability safety guarantees are provided based on the statistics of the Gaussian Process. A learning controller is designed to efficiently explore those uncertain states and expand the barrier certified safe region based on an adaptive sampling scheme. In addition, a recursive Gaussian Process prediction method is developed to learn the complex quadrotor dynamics in real-time. Simulation results are provided to demonstrate the effectiveness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "multi sparse gaussian process learning based semi parametric control"):
  with members:
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-2 (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"
    - "a lyapunov based approach to safe reinforcement learning"
    - "accelerating imitation learning with predictive models"

Connected through hyperedge HE-3 (induced by source paper "barrier certified adaptive reinforcement learning with applications to brushbot navigation"):
  with members:
    - "constrained policy optimization"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "safe policy search for lifelong reinforcement learning with sublinear regret"
    - "adaptive learning in cartesian product of reproducing kernel hilbert spaces"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"
    - "practical kernel based reinforcement learning"

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"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "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"
    - "least squares temporal difference learning for the linear quadratic regulator"

Connected through hyperedge HE-5 (induced by source paper "learning control lyapunov functions from counterexamples and demonstrations"):
  with members:
    - "path following through control funnel functions"
    - "learning lyapunov potential functions from counterexamples and demonstrations"
    - "a theory of formal synthesis via inductive learning"
    - "counterexample guided synthesis of switched controllers for reach while stay properties"
    - "controller synthesis with inductive proofs for piecewise linear systems an smt based algorithm"
    - "counter example guided synthesis of control lyapunov functions for switched systems"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "torque saturation in bipedal robotic walking through control lyapunov function based quadratic programs"

Connected through hyperedge HE-6 (induced by source paper "continuous time signal temporal logic planning with control barrier function"):
  with members:
    - "enhancing the performance of a safe controller via supervised learning for truck lateral control"
    - "safety barrier certificates for heterogeneous multi robot systems"
    - "robust temporal logic model predictive control"

Connected through hyperedge HE-7 (induced by source paper "barrier functions in cascaded controller safe quadrotor control"):
  with members:
    - "safe certificate based maneuvers for teams of quadrotors using differential flatness"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"

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)