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: "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
  Abstract: We present MonoPSR, a monocular 3D object detection method that leverages proposals and shape reconstruction. First, using the fundamental relations of a pinhole camera model, detections from a mature 2D object detector are used to generate a 3D proposal per object in a scene. The 3D location of these proposals prove to be quite accurate, which greatly reduces the difficulty of regressing the final 3D bounding box detection. Simultaneously, a point cloud is predicted in an object centered coordinate system to learn local scale and shape information. However, the key challenge is how to exploit shape information to guide 3D localization. As such, we devise aggregate losses, including a novel projection alignment loss, to jointly optimize these tasks in the neural network to improve 3D localization accuracy. We validate our method on the KITTI benchmark where we set new state-of-the-art results among published monocular methods, including the harder pedestrian and cyclist classes, while maintaining efficient run-time.

Connected through hyperedge HE-1 (induced by source paper "cps class level 6d pose and shape estimation from monocular images"):
  with members:
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "disentangling monocular 3d object detection"
    - "multi path learning for object pose estimation across domains"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "mesh r cnn"
    - "normalized object coordinate space for category level 6d object pose and size estimation"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "sdod real time segmenting and detecting 3d objects by depth"):
  with members:
    - "sdnet semantically guided depth estimation network"
    - "yolact better real time instance segmentation"
    - "mask scoring r cnn"
    - "fcos fully convolutional one stage object detection"
    - "solo segmenting objects by locations"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "deep ordinal regression network for monocular depth estimation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-5 (induced by source paper "smoke single stage monocular 3d object detection via keypoint estimation"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-6 (induced by source paper "confidence guided stereo 3d object detection with split depth estimation"):
  with members:
    - "dsgn deep stereo geometry network for 3d object detection"
    - "canadian adverse driving conditions dataset"
    - "object centric stereo matching for 3d object detection"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pv rcnn point voxel feature set abstraction for 3d object detection"
    - "task aware monocular depth estimation for 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "scalability in perception for autonomous driving waymo open dataset"

Connected through hyperedge HE-7 (induced by source paper "sianms non maximum suppression with siamese networks for multi camera 3d object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "scalability in perception for autonomous driving waymo open dataset"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "m3d rpn monocular 3d region proposal network for object detection"
    - "single stage monocular 3d object detection with virtual cameras"
    - "birdnet a 3d object detection framework from lidar information"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-8 (induced by source paper "monocular 3d object detection with decoupled structured polygon estimation and height guided depth estimation"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "deep ordinal regression network for monocular depth estimation"
    - "monogrnet a geometric reasoning network for monocular 3d object localization"
    - "roi 10d monocular lifting of 2d detection to 6d pose and metric shape"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"

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)