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: "amz driverless the full autonomous racing system"
  Abstract: This paper presents the algorithms and system architecture of an autonomous racecar. The introduced vehicle is powered by a software stack designed for robustness, reliability, and extensibility. In order to autonomously race around a previously unknown track, the proposed solution combines state of the art techniques from different fields of robotics. Specifically, perception, estimation, and control are incorporated into one high-performance autonomous racecar. This complex robotic system, developed by AMZ Driverless and ETH Zurich, finished 1st overall at each competition we attended: Formula Student Germany 2017, Formula Student Italy 2018 and Formula Student Germany 2018. We discuss the findings and learnings from these competitions and present an experimental evaluation of each module of our solution.

Connected through hyperedge HE-1 (induced by source paper "practical reinforcement learning for mpc learning from sparse objectives in under an hour on a real robot"):
  with members:
    - "deep value model predictive control"
    - "data driven economic nmpc using reinforcement learning"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "learning based model predictive control for safe exploration"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "cautious model predictive control using gaussian process regression"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "fast and accurate mapping for autonomous racing"):
  with members:
    - "tracking multiple moving objects using unscented kalman filtering techniques"
    - "yolov3 an incremental improvement"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "redundant perception and state estimation for reliable autonomous racing"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "sampling based algorithms for optimal motion planning using closed loop prediction"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (induced by source paper "end to end velocity estimation for autonomous racing"):
  with members:
    - "vision based high speed driving with a deep dynamic observer"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "redundant perception and state estimation for reliable autonomous racing"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "adadelta an adaptive learning rate method"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "optimization based hierarchical motion planning for autonomous racing"):
  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"

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)