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: "vision based high speed driving with a deep dynamic observer"
  Abstract: In this paper we present a framework for combining deep learning-based road detection, particle filters, and Model Predictive Control (MPC) to drive aggressively using only a monocular camera, IMU, and wheel speed sensors. This framework uses deep convolutional neural networks combined with LSTMs to learn a local cost map representation of the track in front of the vehicle. A particle filter uses this dynamic observation model to localize in a schematic map, and MPC is used to drive aggressively using this particle filter based state estimate. We show extensive real world testing results, and demonstrate reliable operation of the vehicle at the friction limits on a complex dirt track. We reach speeds above 27 mph (12 m/s) on a dirt track with a 105 foot (32m) long straight using our 1:5 scale test vehicle. A video of these results can be found at this https URL

Connected through hyperedge HE-1 (induced by source paper "end to end velocity estimation for autonomous racing"):
  with members:
    - "amz driverless the full autonomous racing system"
    - "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-2 (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-3 (induced by source paper "combining optimal control and learning for visual navigation in novel environments"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "deep drone racing learning agile flight in dynamic environments"
    - "learning navigation behaviors end to end with autorl"
    - "semi parametric topological memory for navigation"
    - "variational end to end navigation and localization"

Connected through hyperedge HE-4 (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"
    - "autorally an open platform for aggressive autonomous driving"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "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-5 (induced by source paper "vision based autonomous vehicle control using the two point visual driver control model"):
  with members:
    - "autorally an open platform for aggressive autonomous driving"
    - "lanenet real time lane detection networks for autonomous driving"
    - "yolo9000 better faster stronger"
    - "fast r cnn"
    - "adam a method for stochastic optimization"

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)