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: "reconstructing vechicles from a single image shape priors for road scene understanding"
  Abstract: We present an approach for reconstructing vehicles from a single (RGB) image, in the context of autonomous driving. Though the problem appears to be ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles project to an image can be used to reason about the reverse process, i.e., how shapes (back-)project from 2D to 3D. We encode this knowledge in \emph{shape priors}, which are learnt over a small keypoint-annotated dataset. We then formulate a shape-aware adjustment problem that uses the learnt shape priors to recover the 3D pose and shape of a query object from an image. For shape representation and inference, we leverage recent successes of Convolutional Neural Networks (CNNs) for the task of object and keypoint localization, and train a novel cascaded fully-convolutional architecture to localize vehicle \emph{keypoints} in images. The shape-aware adjustment then robustly recovers shape (3D locations of the detected keypoints) while simultaneously filling in occluded keypoints. To tackle estimation errors incurred due to erroneously detected keypoints, we use an Iteratively Re-weighted Least Squares (IRLS) scheme for robust optimization, and as a by-product characterize noise models for each predicted keypoint. We evaluate our approach on autonomous driving benchmarks, and present superior results to existing monocular, as well as stereo approaches.

Connected through hyperedge HE-1 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "foveabox beyond anchor based object detector"
    - "pixor real time 3d object detection from point clouds"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "shift r cnn deep monocular 3d object detection with closed form geometric constraints"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "cubeslam monocular 3 d object slam"

Connected through hyperedge HE-2 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "disentangling monocular 3d object detection"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "monocular 3d object detection via geometric reasoning on keypoints"
    - "fcos fully convolutional one stage object detection"
    - "m3d rpn monocular 3d region proposal network for object detection"

Connected through hyperedge HE-3 (induced by source paper "rodnet object detection under severe conditions using vision radio cross modal supervision"):
  with members:
    - "video instance segmentation"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "centernet keypoint triplets for object detection"
    - "tracking without bells and whistles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-4 (induced by source paper "multi object monocular slam for dynamic environments"):
  with members:
    - "cubeslam monocular 3 d object slam"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "the earth ain t flat monocular reconstruction of vehicles on steep and graded roads from a moving camera"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-5 (induced by source paper "disentangling monocular 3d object detection"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "monocular 3d object detection via geometric reasoning on keypoints"
    - "cornernet detecting objects as paired keypoints"
    - "deep learning for generic object detection a survey"

Connected through hyperedge HE-6 (induced by source paper "single stage monocular 3d object detection with virtual cameras"):
  with members:
    - "disentangling monocular 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "m3d rpn monocular 3d region proposal network for object detection"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"

Connected through hyperedge HE-7 (induced by source paper "stereo r cnn based 3d object detection for autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "pyramid stereo matching network"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "cubeslam monocular 3d object detection and slam without prior models"):
  with members:
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "robust dense mapping for large scale dynamic environments"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "bayesian scale estimation for monocular slam based on generic object detection for correcting scale drift"
    - "direct monocular odometry using points and lines"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "pop up slam semantic monocular plane slam for low texture environments"

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)