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: "point linking network for object detection"
  Abstract: Object detection is a core problem in computer vision. With the development of deep ConvNets, the performance of object detectors has been dramatically improved. The deep ConvNets based object detectors mainly focus on regressing the coordinates of bounding box, e.g., Faster-R-CNN, YOLO and SSD. Different from these methods that considering bounding box as a whole, we propose a novel object bounding box representation using points and links and implemented using deep ConvNets, termed as Point Linking Network (PLN). Specifically, we regress the corner/center points of bounding-box and their links using a fully convolutional network; then we map the corner points and their links back to multiple bounding boxes; finally an object detection result is obtained by fusing the multiple bounding boxes. PLN is naturally robust to object occlusion and flexible to object scale variation and aspect ratio variation. In the experiments, PLN with the Inception-v2 model achieves state-of-the-art single-model and single-scale results on the PASCAL VOC 2007, the PASCAL VOC 2012 and the COCO detection benchmarks without bells and whistles. The source code will be released.

Connected through hyperedge HE-1 (induced by source paper "penet object detection using points estimation in aerial images"):
  with members:
    - "clustered object detection in aerial images"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "objects as points"
    - "augmentation for small object detection"
    - "vision meets drones a challenge"
    - "cornernet detecting objects as paired keypoints"
    - "yolo lite a real time object detection algorithm optimized for non gpu computers"
    - "detnet a backbone network for object detection"

Connected through hyperedge HE-2 (induced by source paper "cornernet lite efficient keypoint based object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "autofocus efficient multi scale inference"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-3 (induced by source paper "bottom up object detection by grouping extreme and center points"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "acquisition of localization confidence for accurate object detection"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "path aggregation network for instance segmentation"
    - "couplenet coupling global structure with local parts for object detection"

Connected through hyperedge HE-4 (induced by source paper "learning to reconstruct 3d manhattan wireframes from a single image"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "megadepth learning single view depth prediction from internet photos"
    - "semantic scene completion from a single depth image"
    - "stacked hourglass networks for human pose estimation"
    - "single image 3d interpreter network"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "cornernet detecting objects as paired keypoints"):
  with members:
    - "acquisition of localization confidence for accurate object detection"
    - "improving object detection from scratch via gated feature reuse"
    - "soft nms improving object detection with one line of code"
    - "couplenet coupling global structure with local parts for object detection"
    - "improving object localization with fitness nms and bounded iou loss"
    - "feature selective networks for object detection"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-6 (induced by source paper "multi oriented scene text detection via corner localization and region segmentation"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "wordsup exploiting word annotations for character based text detection"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"
    - "dssd deconvolutional single shot detector"
    - "wetext scene text detection under weak supervision"
    - "integrating scene text and visual appearance for fine grained image classification"

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)