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: "aggressive deep driving model predictive control with a cnn cost model"
  Abstract: We present a framework for vision-based model predictive control (MPC) for the task of aggressive, high-speed autonomous driving. Our approach uses deep convolutional neural networks to predict cost functions from input video which are directly suitable for online trajectory optimization with MPC. We demonstrate the method in a high speed autonomous driving scenario, where we use a single monocular camera and a deep convolutional neural network to predict a cost map of the track in front of the vehicle. Results are demonstrated on a 1:5 scale autonomous vehicle given the task of high speed, aggressive driving.

Connected through hyperedge HE-1 (induced by source paper "multi task regression based learning for autonomous unmanned aerial vehicle flight control within unstructured outdoor environments"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "deep drone racing learning agile flight in dynamic environments"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "unsupervised learning of depth and ego motion from video"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for time optimal velocity control using prior knowledge"):
  with members:
    - "deep q learning from demonstrations"
    - "a simple dynamic model for aggressive near limits trajectory planning"
    - "end to end learning for self driving cars"
    - "learning model predictive control for iterative tasks a data driven control framework"
    - "continuous control with deep reinforcement learning"
    - "deepdriving learning affordance for direct perception in autonomous driving"
    - "a self supervised terrain roughness estimator for off road autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "coupled longitudinal and lateral control of a vehicle using deep learning"):
  with members:
    - "end to end learning for self driving cars"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "agile autonomous driving using end to end deep imitation learning"):
  with members:
    - "convergence of value aggregation for imitation learning"
    - "fast policy learning through imitation and reinforcement"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "comparing human centric and robot centric sampling for robot deep learning from demonstrations"
    - "end to end learning for self driving cars"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "plato policy learning using adaptive trajectory optimization"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-5 (induced by source paper "deep predictive models for collision risk assessment in autonomous driving"):
  with members:
    - "an empirical evaluation of deep learning on highway driving"
    - "joint belief and intent prediction for collision avoidance in autonomous vehicles"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "generating sequences with recurrent neural networks"
    - "scene parsing with multiscale feature learning purity trees and optimal covers"

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)