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: "t cnn tubelets with convolutional neural networks for object detection from videos"
  Abstract: The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks, such as GoogleNet and VGG, novel object detection frameworks, such as R-CNN and its successors, Fast R-CNN, and Faster R-CNN, play an essential role in improving the state of the art. Despite their effectiveness on still images, those frameworks are not specifically designed for object detection from videos. Temporal and contextual information of videos are not fully investigated and utilized. In this paper, we propose a deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing still-image detection frameworks when they are applied to videos. It is called T-CNN, i.e., tubelets with convolutional neueral networks. The proposed framework won newly introduced an object-detection-from-video task with provided data in the ImageNet Large-Scale Visual Recognition Challenge 2015. Code is publicly available at  https://github.com/myfavouritekk/T-CNN .

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "relation distillation networks for video object detection"):
  with members:
    - "exploring object relation in mean teacher for cross domain detection"
    - "cornernet detecting objects as paired keypoints"
    - "object detection in video with spatiotemporal sampling networks"
    - "videos as space time region graphs"
    - "exploring visual relationship for image captioning"
    - "non local neural networks"
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video object detection"

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"
    - "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"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-5 (induced by source paper "object detection with convolutional neural networks"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "vision meets drones a challenge"
    - "deep learning for generic object detection a survey"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "yolov3 an incremental improvement"
    - "quantization mimic towards very tiny cnn for object detection"
    - "learning depth from single images with deep neural network embedding focal length"

Connected through hyperedge HE-6 (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"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-7 (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"
    - "dual attention network for scene segmentation"
    - "non local neural networks"
    - "towards high performance video object detection"

Connected through hyperedge HE-8 (induced by source paper "approxnet content and contention aware video analytics system for the edge"):
  with members:
    - "focus querying large video datasets with low latency and low cost"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale dense networks for resource efficient image classification"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deeppicar a low cost deep neural network based autonomous car"

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)