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 from maps visual common sense for autonomous driving"
  Abstract: Today's autonomous vehicles rely extensively on high-definition 3D maps to navigate the environment. While this approach works well when these maps are completely up-to-date, safe autonomous vehicles must be able to corroborate the map's information via a real time sensor-based system. Our goal in this work is to develop a model for road layout inference given imagery from on-board cameras, without any reliance on high-definition maps. However, no sufficient dataset for training such a model exists. Here, we leverage the availability of standard navigation maps and corresponding street view images to construct an automatically labeled, large-scale dataset for this complex scene understanding problem. By matching road vectors and metadata from navigation maps with Google Street View images, we can assign ground truth road layout attributes (e.g., distance to an intersection, one-way vs. two-way street) to the images. We then train deep convolutional networks to predict these road layout attributes given a single monocular RGB image. Experimental evaluation demonstrates that our model learns to correctly infer the road attributes using only panoramas captured by car-mounted cameras as input. Additionally, our results indicate that this method may be suitable to the novel application of recommending safety improvements to infrastructure (e.g., suggesting an alternative speed limit for a street).

Connected through hyperedge HE-1 (induced by source paper "learning a layout transfer network for context aware object detection"):
  with members:
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "a general pipeline for 3d detection of vehicles"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "seeing what is not there learning context to determine where objects are missing"
    - "dsod learning deeply supervised object detectors from scratch"
    - "foveanet perspective aware urban scene parsing"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"

Connected through hyperedge HE-2 (induced by source paper "long range indoor navigation with prm rl"):
  with members:
    - "learning navigation behaviors end to end with autorl"
    - "pearl preference appraisal reinforcement learning for motion planning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "deepnav learning to navigate large cities"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"

Connected through hyperedge HE-3 (induced by source paper "deepgoal learning to drive with driving intention from human control demonstration"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "variational end to end navigation and localization"
    - "laser map aided visual inertial localization in changing environment"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "3d ssd learning hierarchical features from rgb d images for amodal 3d object detection"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-4 (induced by source paper "you are here geolocation by embedding maps and images"):
  with members:
    - "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"
    - "variational end to end navigation and localization"
    - "automated map reading image based localisation in 2 d maps using binary semantic descriptors"
    - "in defense of the triplet loss for person re identification"
    - "find your way by observing the sun and other semantic cues"
    - "localizing and orienting street views using overhead imagery"
    - "semantic image based geolocation given a map"
    - "learning deep structure preserving image text embeddings"

Connected through hyperedge HE-5 (induced by source paper "affordance learning in direct perception for autonomous driving"):
  with members:
    - "conditional affordance learning for driving in urban environments"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "carla an open urban driving simulator"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "end to end learning for self driving cars"
    - "places an image database for deep scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised road affordances inference and learning in scenes without traffic signs"):
  with members:
    - "how shall i drive interaction modeling and motion planning towards empathetic and socially graceful driving"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "juncnet a deep neural network for road junction disambiguation for autonomous vehicles"
    - "conditional affordance learning for driving in urban environments"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "laser map aided visual inertial localization in changing environment"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "exploring openstreetmap availability for driving environment understanding"):
  with members:
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "end to end learning of driving models with surround view cameras and route planners"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "failure prediction for autonomous driving"
    - "automated map reading image based localisation in 2 d maps using binary semantic descriptors"
    - "computer vision for autonomous vehicles problems datasets and state of the art"

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)