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: "autorally an open platform for aggressive autonomous driving"
  Abstract: This article presents AutoRally, a 1$:$5 scale robotics testbed for autonomous vehicle research. AutoRally is designed for robustness, ease of use, and reproducibility, so that a team of two people with limited knowledge of mechanical engineering, electrical engineering, and computer science can construct and then operate the testbed to collect real world autonomous driving data in whatever domain they wish to study. Complete documentation to construct and operate the platform is available online along with tutorials, example controllers, and a driving dataset collected at the Georgia Tech Autonomous Racing Facility. Offline estimation algorithms are used to determine parameters for physics-based dynamics models using an adaptive limited memory joint state unscented Kalman filter. Online vehicle state estimation using a factor graph optimization scheme and a convolutional neural network for semantic segmentation of drivable surface are presented. All algorithms are tested with real world data from the fleet of six AutoRally robots at the Georgia Tech Autonomous Racing Facility tracks, and serve as a demonstration of the robot$'$s capabilities.

Connected through hyperedge HE-1 (induced by source paper "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "f1 10 an open source autonomous cyber physical platform"
    - "mushr a low cost open source robotic racecar for education and research"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "self driving scale car trained by deep reinforcement learning"
    - "an online learning approach to model predictive control"
    - "learning agile and dynamic motor skills for legged robots"
    - "demonstration guided deep reinforcement learning of control policies for dexterous human robot interaction"

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"
    - "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"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-3 (induced by source paper "perceptual attention based predictive control"):
  with members:
    - "ensemble bayesian decision making with redundant deep perceptual control policies"
    - "safe end to end imitation learning for model predictive control"
    - "agile autonomous driving using end to end deep imitation learning"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "mask r cnn"
    - "end to end learning for self driving cars"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-4 (induced by source paper "locally weighted regression pseudo rehearsal for online learning of vehicle dynamics"):
  with members:
    - "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"
    - "learning to adapt meta learning for model based control"
    - "continual learning with deep generative replay"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "fearnet brain inspired model for incremental learning"
    - "deep visual foresight for planning robot motion"
    - "icarl incremental classifier and representation learning"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-5 (induced by source paper "uncertainty aware driver trajectory prediction at urban intersections"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "failing to learn autonomously identifying perception failures for self driving cars"
    - "variational end to end navigation and localization"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "an online learning approach to model predictive control"):
  with members:
    - "introduction to online convex optimization"
    - "mirror descent search and its acceleration"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "vision based high speed driving with a deep dynamic observer"
    - "acceleration of gradient based path integral method for efficient optimal and inverse optimal control"
    - "adaptive online learning in dynamic environments"
    - "risk sensitive path integral control"
    - "theoretical foundation for cma es from information geometric perspective"

Connected through hyperedge HE-7 (induced by source paper "vision based autonomous vehicle control using the two point visual driver control model"):
  with members:
    - "vision based high speed driving with a deep dynamic observer"
    - "lanenet real time lane detection networks for autonomous driving"
    - "yolo9000 better faster stronger"
    - "fast r cnn"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "vision based high speed driving with a deep dynamic observer"):
  with members:
    - "agile autonomous driving using end to end deep imitation learning"
    - "end to end learning for self driving cars"
    - "visual closed loop control for pouring liquids"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "learning deconvolution network for semantic segmentation"
    - "deepdriving learning affordance for direct perception in autonomous driving"
    - "orb slam a versatile and accurate monocular slam system"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

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)