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: "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
  Abstract: We present a method to infer 3D pose and shape of vehicles from a single image. To tackle this ill-posed problem, we optimize two-scale projection consistency between the generated 3D hypotheses and their 2D pseudo-measurements. Specifically, we use a morphable wireframe model to generate a fine-scaled representation of vehicle shape and pose. To reduce its sensitivity to 2D landmarks, we jointly model the 3D bounding box as a coarse representation which improves robustness. We also integrate three task priors, including unsupervised monocular depth, a ground plane constraint as well as vehicle shape priors, with forward projection errors into an overall energy function.

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"
    - "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"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-2 (induced by source paper "learning depth guided convolutions for monocular 3d object detection"):
  with members:
    - "disentangling monocular 3d object detection"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "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"
    - "voxel fpn multi scale voxel feature aggregation in 3d object detection from point clouds"
    - "learning guided convolutional network for depth completion"

Connected through hyperedge HE-3 (induced by source paper "disentangling monocular 3d object detection"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "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"
    - "pointpillars fast encoders for object detection from point clouds"

Connected through hyperedge HE-4 (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"
    - "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"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-5 (induced by source paper "focusing and diffusion bidirectional attentive graph convolutional networks for skeleton based action recognition"):
  with members:
    - "geonet deep geodesic networks for point cloud analysis"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "a comprehensive survey on graph neural networks"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "flownet3d learning scene flow in 3d point clouds"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"

Connected through hyperedge HE-6 (induced by source paper "perspectivenet 3d object detection from a single rgb image via perspective points"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "visual object networks image generation with disentangled 3d representation"
    - "3d aware scene manipulation via inverse graphics"
    - "learning to reconstruct shapes from unseen classes"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-7 (induced by source paper "lates latent space distillation for teacher student driving policy learning"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "learning by cheating"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "learning to steer by mimicking features from heterogeneous auxiliary networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "virtual to real reinforcement learning for autonomous driving"
    - "carla an open urban driving simulator"

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)