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: "path following through control funnel functions"
  Abstract: We present an approach to path following using so-called control funnel functions. Synthesizing controllers to "robustly" follow a reference trajectory is a fundamental problem for autonomous vehicles. Robustness, in this context, requires our controllers to handle a specified amount of deviation from the desired trajectory. Our approach considers a timing law that describes how fast to move along a given reference trajectory and a control feedback law for reducing deviations from the reference. We synthesize both feedback laws using "control funnel functions" that jointly encode the control law as well as its correctness argument over a mathematical model of the vehicle dynamics. We adapt a previously described demonstration-based learning algorithm to synthesize a control funnel function as well as the associated feedback law. We implement this law on top of a 1/8th scale autonomous vehicle called the Parkour car. We compare the performance of our path following approach against a trajectory tracking approach by specifying trajectories of varying lengths and curvatures. Our experiments demonstrate the improved robustness obtained from the use of control funnel functions.

Connected through hyperedge HE-1 (induced by source paper "learning control lyapunov functions from counterexamples and demonstrations"):
  with members:
    - "safe learning of quadrotor dynamics using barrier certificates"
    - "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-2 (induced by source paper "inductive certificate synthesis for control design"):
  with members:
    - "learning control lyapunov functions from counterexamples and demonstrations"
    - "the reach avoid problem for constant rate multi mode systems"
    - "automated formal synthesis of digital controllers for state space physical plants"
    - "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"

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)