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: "centernet keypoint triplets for object detection"
  Abstract: In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions. This paper presents an efficient solution which explores the visual patterns within each cropped region with minimal costs. We build our framework upon a representative one-stage keypoint-based detector named CornerNet. Our approach, named CenterNet, detects each object as a triplet, rather than a pair, of keypoints, which improves both precision and recall. Accordingly, we design two customized modules named cascade corner pooling and center pooling, which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions, respectively. On the MS-COCO dataset, CenterNet achieves an AP of 47.0%, which outperforms all existing one-stage detectors by at least 4.9%. Meanwhile, with a faster inference speed, CenterNet demonstrates quite comparable performance to the top-ranked two-stage detectors. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "revisiting the sibling head in object detector"):
  with members:
    - "reppoints point set representation for object detection"
    - "double head rcnn rethinking classification and localization for object detection"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "dropblock a regularization method for convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "arbitrary oriented object detection with circular smooth label"):
  with members:
    - "objects detection for remote sensing images based on polar coordinates"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "ienet interacting embranchment one stage anchor free detector for orientation aerial object detection"
    - "adaptive period embedding for representing oriented objects in aerial images"
    - "r3det refined single stage detector with feature refinement for rotating object"
    - "gliding vertex on the horizontal bounding box for multi oriented object detection"
    - "cad net a context aware detection network for objects in remote sensing imagery"
    - "learning modulated loss for rotated object detection"

Connected through hyperedge HE-3 (induced by source paper "cpm r cnn calibrating point guided misalignment in object detection"):
  with members:
    - "grid r cnn plus faster and better"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "grid r cnn"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "parsing r cnn for instance level human analysis"

Connected through hyperedge HE-4 (induced by source paper "actions as moving points"):
  with members:
    - "tacnet transition aware context network for spatio temporal action detection"
    - "step spatio temporal progressive learning for video action detection"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "simple baselines for human pose estimation and tracking"
    - "actor centric relation network"

Connected through hyperedge HE-5 (induced by source paper "mobilepose real time pose estimation for unseen objects with weak shape supervision"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "hybridpose 6d object pose estimation under hybrid representations"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "object as distribution"
    - "mediapipe a framework for building perception pipelines"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "objects as points"

Connected through hyperedge HE-6 (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"
    - "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"
    - "exploit the connectivity multi object tracking with trackletnet"

Connected through hyperedge HE-7 (induced by source paper "crowd scene analysis by output encoding"):
  with members:
    - "point in box out beyond counting persons in crowds"
    - "selective kernel networks"
    - "scale aware trident networks for object detection"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "where are the blobs counting by localization with point supervision"
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"

Connected through hyperedge HE-8 (induced by source paper "spotnet self attention multi task network for object detection"):
  with members:
    - "road user detection in videos"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "cascade r cnn delving into high quality object detection"

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)