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: "object detection in videos by high quality object linking"
  Abstract: Compared with object detection in static images, object detection in videos is more challenging due to degraded image qualities. An effective way to address this problem is to exploit temporal contexts by linking the same object across video to form tubelets and aggregating classification scores in the tubelets. In this paper, we focus on obtaining high quality object linking results for better classification. Unlike previous methods that link objects by checking boxes between neighboring frames, we propose to link in the same frame. To achieve this goal, we extend prior methods in following aspects: (1) a cuboid proposal network that extracts spatio-temporal candidate cuboids which bound the movement of objects; (2) a short tubelet detection network that detects short tubelets in short video segments; (3) a short tubelet linking algorithm that links temporally-overlapping short tubelets to form long tubelets. Experiments on the ImageNet VID dataset show that our method outperforms both the static image detector and the previous state of the art. In particular, our method improves results by 8.8% over the static image detector for fast moving objects.

Connected through hyperedge HE-1 (induced by source paper "proposal learning for semi supervised object detection"):
  with members:
    - "momentum contrast for unsupervised visual representation learning"
    - "deep high resolution representation learning for visual recognition"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "mask scoring r cnn"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "unsupervised data augmentation for consistency training"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-2 (induced by source paper "mask scoring r cnn"):
  with members:
    - "ccnet criss cross attention for semantic segmentation"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "acquisition of localization confidence for accurate object detection"
    - "object detection in videos by short and long range object linking"
    - "segmentation aware convolutional networks using local attention masks"
    - "improving object localization with fitness nms and bounded iou loss"
    - "deformable convolutional networks"
    - "masklab instance segmentation by refining object detection with semantic and direction features"

Connected through hyperedge HE-3 (induced by source paper "object detection in video with spatial temporal context aggregation"):
  with members:
    - "universal transformers"
    - "object detection in video with spatiotemporal sampling networks"
    - "optimizing video object detection via a scale time lattice"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "non local neural networks"
    - "flow guided feature aggregation for video object detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "geometry aware video object detection for static cameras"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "tube cnn modeling temporal evolution of appearance for object detection in video"
    - "object detection in video with spatiotemporal sampling networks"
    - "carla an open urban driving simulator"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "detect to track and track to detect"
    - "object detection from video tubelets with convolutional neural networks"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "detect or track towards cost effective video object detection tracking"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "dynamic video segmentation network"
    - "optimizing video object detection via a scale time lattice"
    - "low latency video semantic segmentation"
    - "unveiling the power of deep tracking"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video 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)