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: "autonomous racing using learning model predictive control"
  Abstract: A novel learning Model Predictive Control technique is applied to the autonomous racing problem. The goal of the controller is to minimize the time to complete a lap. The proposed control strategy uses the data from previous laps to improve its performance while satisfying safety requirements. Moreover, a system identification technique is proposed to estimate the vehicle dynamics. Simulation results with the high fidelity simulator software CarSim show the effectiveness of the proposed control scheme.

Connected through hyperedge HE-1 (induced by source paper "extending deep model predictive control with safety augmented value estimation from demonstrations"):
  with members:
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "sample based learning model predictive control for linear uncertain systems"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "playing hard exploration games by watching youtube"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-2 (induced by source paper "amz driverless the full autonomous racing system"):
  with members:
    - "real time 3d traffic cone detection for autonomous driving"
    - "autorally an open platform for aggressive autonomous driving"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "redundant perception and state estimation for reliable autonomous racing"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "real time control for autonomous racing based on viability theory"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (induced by source paper "safety augmented value estimation from demonstrations saved safe deep model based rl for sparse cost robotic tasks"):
  with members:
    - "on policy robot imitation learning from a converging supervisor"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "hindsight experience replay"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-4 (induced by source paper "adaptive trajectory planning and optimization at limits of handling"):
  with members:
    - "adaptive mpc with chance constraints for fir systems"
    - "learning based model predictive control for safe exploration"
    - "adaptive mpc for autonomous lane keeping"
    - "adaptive mpc for iterative tasks"
    - "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"
    - "a sequential two step algorithm for fast generation of vehicle racing trajectories"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-5 (induced by source paper "safe and near optimal policy learning for model predictive control using primal dual neural networks"):
  with members:
    - "learning an approximate model predictive controller with guarantees"
    - "adaptive mpc for autonomous lane keeping"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

Connected through hyperedge HE-6 (induced by source paper "learning how to autonomously race a car a predictive control approach"):
  with members:
    - "real time control for autonomous racing based on viability theory"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control"

Connected through hyperedge HE-7 (induced by source paper "learning to adapt meta learning for model based control"):
  with members:
    - "model ensemble trust region policy optimization"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning to learn meta critic networks for sample efficient learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "rapid adaptation with conditionally shifted neurons"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "a simple neural attentive meta learner"

Connected through hyperedge HE-8 (induced by source paper "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"):
  with members:
    - "cautious model predictive control using gaussian process regression"
    - "learning model predictive control for iterative tasks"
    - "learning model predictive control for iterative tasks a data driven control framework"
    - "path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control"

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)