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: "learning driving models with a surround view camera system and a route planner"
  Abstract: For people, having a rear-view mirror and side-view mirrors is vital for safe driving. They deliver a better view of what happens around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task quite intractable. Hence, we investigate the problem with a more realistic setting, which consists of a surround-view camera system with eight cameras, a route planner, and a CAN bus reader. In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and the low-level driving maneuvers (e.g. steering angle and speed) by human drivers. With such sensor setup we collect a new driving dataset, covering diverse driving scenarios and varying weather/illumination conditions. Finally, we learn a novel driving model by integrating information from the surround-view cameras and the route planner. Two route planners are exploited: one based on OpenStreetMap and the other on TomTom Maps. The route planners are exploited in two ways: 1) by representing the planned routes as a stack of GPS coordinates, and 2) by rendering the planned routes on a map and recording the progression into a video. Our experiments show that: 1) 360-degree surround-view cameras help avoid failures made with a single front-view camera for the driving task; and 2) a route planner helps the driving task significantly. We acknowledge that our method is not the best-ever driving model, but that is not our focus. Rather, it provides a strong basis for further academic research, especially on driving relevant tasks by integrating information from street-view images and the planned driving routes. Code and data will be made available.

Connected through hyperedge HE-1 (induced by source paper "failure prediction for autonomous driving"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "anticipating traffic accidents with adaptive loss and large scale incident db"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "predicting deeper into the future of semantic segmentation"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "brain inspired cognitive model with attention for self driving cars"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-2 (induced by source paper "model adaptation with synthetic and real data for semantic dense foggy scene understanding"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "joint transmission map estimation and dehazing using deep networks"
    - "the cityscapes dataset for semantic urban scene understanding"

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.CV(Computer Vision and Pattern Recognition)