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: "a model predictive motion planner for the iara autonomous car"
  Abstract: We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA's MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories so that they follow the path closely and, at the same time, are at a safe distance of occasional obstacles. Our experiments have shown that MPMP is able to compute trajectories that precisely follow a path produced by a Human driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).

Connected through hyperedge HE-1 (induced by source paper "self driving cars a survey"):
  with members:
    - "traffic light recognition using deep learning and prior maps for autonomous cars"
    - "effortless deep training for traffic sign detection using templates and arbitrary natural images"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "dct maps compact differentiable lidar maps based on the cosine transform"
    - "visual global localization with a hybrid wnn cnn approach"
    - "map memorization and forgetting in the iara autonomous car"
    - "mapping road lanes using laser remission and deep neural networks"
    - "roadtracer automatic extraction of road networks from aerial images"

Connected through hyperedge HE-2 (induced by source paper "mapping road lanes using laser remission and deep neural networks"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "automatic large scale data acquisition via crowdsourcing for crosswalk classification a deep learning approach"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "high speed trajectory planning for autonomous vehicles using a simple dynamic model"):
  with members:
    - "a simple dynamic model for aggressive near limits trajectory planning"

Connected through hyperedge HE-4 (induced by source paper "a simple dynamic model for aggressive near limits trajectory planning"):
  with members:
    - "high speed trajectory planning for autonomous vehicles using a simple dynamic model"

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)