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: "cornernet detecting objects as paired keypoints"
  Abstract: We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners. Experiments show that CornerNet achieves a 42.2% AP on MS COCO, outperforming all existing one-stage detectors.

Connected through hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark"):
  with members:
    - "gabor convolutional networks"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "deep adaptive proposal network for object detection in optical remote sensing images"
    - "cross view image matching for geo localization in urban environments"

Connected through hyperedge HE-2 (induced by source paper "siamese attentional keypoint network for high performance visual tracking"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "learning reinforced attentional representation for end to end visual tracking"
    - "learning cascaded siamese networks for high performance visual tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient multi level correlating for visual tracking"
    - "high performance visual tracking with circular and structural operators"

Connected through hyperedge HE-3 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "robust adversarial perturbation on deep proposal based models"

Connected through hyperedge HE-4 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps"):
  with members:
    - "3d point cloud processing and learning for autonomous driving"
    - "point cloud processing via recurrent set encoding"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "pixor real time 3d object detection from point clouds"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking"):
  with members:
    - "gridmask data augmentation"
    - "reppoints point set representation for object detection"
    - "res2net a new multi scale backbone architecture"
    - "foveabox beyond anchor based object detector"
    - "making convolutional networks shift invariant again"
    - "region proposal by guided anchoring"
    - "mixconv mixed depthwise convolutional kernels"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"

Connected through hyperedge HE-6 (induced by source paper "incremental few shot object detection"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "boosting few shot visual learning with self supervision"
    - "large scale long tailed recognition in an open world"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "zero shot detection"
    - "a closer look at few shot classification"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "objects as points"

Connected through hyperedge HE-7 (induced by source paper "siamese box adaptive network for visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "reppoints point set representation for object detection"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "gradnet gradient guided network for visual object tracking"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "spm tracker series parallel matching for real time visual object tracking"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "feature selective anchor free module for single shot object detection"

Connected through hyperedge HE-8 (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"
    - "centernet keypoint triplets for object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "dropblock a regularization method for convolutional networks"

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)