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 via geometric reasoning on keypoints"
  Abstract: Monocular 3D object detection is well-known to be a challenging vision task due to the loss of depth information; attempts to recover depth using separate image-only approaches lead to unstable and noisy depth estimates, harming 3D detections. In this paper, we propose a novel keypoint-based approach for 3D object detection and localization from a single RGB image. We build our multi-branch model around 2D keypoint detection in images and complement it with a conceptually simple geometric reasoning method. Our network performs in an end-to-end manner, simultaneously and interdependently estimating 2D characteristics, such as 2D bounding boxes, keypoints, and orientation, along with full 3D pose in the scene. We fuse the outputs of distinct branches, applying a reprojection consistency loss during training. The experimental evaluation on the challenging KITTI dataset benchmark demonstrates that our network achieves state-of-the-art results among other monocular 3D detectors.

Connected through hyperedge HE-1 (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"
    - "fcos fully convolutional one stage object detection"
    - "m3d rpn monocular 3d region proposal network for object detection"
    - "objects as points"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "learning to approximate directional fields defined over 2d planes"):
  with members:
    - "procedural synthesis of remote sensing images for robust change detection with neural networks"
    - "boundary loss for remote sensing imagery semantic segmentation"
    - "vectorization of line drawings via polyvector fields"
    - "targeted change detection in remote sensing images"
    - "perceptual deep depth super resolution"
    - "abc a big cad model dataset for geometric deep learning"
    - "u net convolutional networks for biomedical image segmentation"

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)