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 from video tubelets with convolutional neural networks"
  Abstract: Deep Convolution Neural Networks (CNNs) have shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. For object detection, particularly in still images, the performance has been significantly increased last year thanks to powerful deep networks (e.g. GoogleNet) and detection frameworks (e.g. Regions with CNN features (RCNN)). The lately introduced ImageNet [6] task on object detection from video (VID) brings the object detection task into the video domain, in which objects' locations at each frame are required to be annotated with bounding boxes. In this work, we introduce a complete framework for the VID task based on still-image object detection and general object tracking. Their relations and contributions in the VID task are thoroughly studied and evaluated. In addition, a temporal convolution network is proposed to incorporate temporal information to regularize the detection results and shows its effectiveness for the task. Code is available at https://github.com/ myfavouritekk/vdetlib.

Connected through hyperedge HE-1 (induced by source paper "rethinking temporal object detection from robotic perspectives"):
  with members:
    - "joint anchor feature refinement for real time accurate object detection in images and videos"
    - "siamfc towards robust and accurate visual tracking with target estimation guidelines"
    - "atom accurate tracking by overlap maximization"
    - "object detection in video with spatiotemporal sampling networks"
    - "temporally identity aware ssd with attentional lstm"
    - "towards high performance video object detection"
    - "focal loss for dense object detection"
    - "single shot refinement neural network for object detection"

Connected through hyperedge HE-2 (induced by source paper "animal detection in man made environments"):
  with members:
    - "looking fast and slow memory guided mobile video object detection"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "the iwildcam 2019 challenge dataset"
    - "searching for mobilenetv3"
    - "the via annotation software for images audio and video"
    - "large scale interactive object segmentation with human annotators"
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "got 10k a large high diversity benchmark for generic object tracking in the wild"):
  with members:
    - "ptb tir a thermal infrared pedestrian tracking benchmark"
    - "class balanced loss based on effective number of samples"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "long term tracking in the wild a benchmark"
    - "fast and accurate online video object segmentation via tracking parts"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"

Connected through hyperedge HE-4 (induced by source paper "pack and detect fast object detection in videos using region of interest packing"):
  with members:
    - "yolov3 an incremental improvement"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "recurrent residual module for fast inference in videos"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "towards high performance video object detection"

Connected through hyperedge HE-5 (induced by source paper "looking fast and slow memory guided mobile video object detection"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "object detection in video with spatiotemporal sampling networks"
    - "depth adaptive computational policies for efficient visual tracking"
    - "towards high performance video object detection for mobiles"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "optimizing video object detection via a scale time lattice"
    - "low latency video semantic segmentation"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"

Connected through hyperedge HE-6 (induced by source paper "progressive sparse local attention for video object detection"):
  with members:
    - "ocnet object context network for scene parsing"
    - "dynamic video segmentation network"
    - "ccnet criss cross attention for semantic segmentation"
    - "optimizing video object detection via a scale time lattice"
    - "low latency video semantic segmentation"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "dual attention network for scene segmentation"
    - "non local neural networks"

Connected through hyperedge HE-7 (induced by source paper "sequence level semantics aggregation for video object detection"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "yolov3 an incremental improvement"
    - "optimizing video object detection via a scale time lattice"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "videos as space time region graphs"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "non local neural networks"
    - "towards high performance video object detection"

Connected through hyperedge HE-8 (induced by source paper "tracking without bells and whistles"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "features for multi target multi camera tracking and re identification"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "in defense of the triplet loss for person re identification"
    - "an implementation of faster rcnn with study for region sampling"

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)