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: "context matters refining object detection in video with recurrent neural networks"
  Abstract: Given the vast amounts of video available online, and recent breakthroughs in object detection with static images, object detection in video offers a promising new frontier. However, motion blur and compression artifacts cause substantial frame-level variability, even in videos that appear smooth to the eye. Additionally, video datasets tend to have sparsely annotated frames. We present a new framework for improving object detection in videos that captures temporal context and encourages consistency of predictions. First, we train a pseudo-labeler, that is, a domain-adapted convolutional neural network for object detection. The pseudo-labeler is first trained individually on the subset of labeled frames, and then subsequently applied to all frames. Then we train a recurrent neural network that takes as input sequences of pseudo-labeled frames and optimizes an objective that encourages both accuracy on the target frame and consistency across consecutive frames. The approach incorporates strong supervision of target frames, weak-supervision on context frames, and regularization via a smoothness penalty. Our approach achieves mean Average Precision (mAP) of 68.73, an improvement of 7.1 over the strongest image-based baselines for the Youtube-Video Objects dataset. Our experiments demonstrate that neighboring frames can provide valuable information, even absent labels.

Connected through hyperedge HE-1 (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-2 (induced by source paper "real time visual tracking using spatial aware temporal aggregation network"):
  with members:
    - "object detection in video with spatiotemporal sampling networks"
    - "efficient video object segmentation via network modulation"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"
    - "vital visual tracking via adversarial learning"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-3 (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"
    - "object detection in videos by high quality object linking"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "embryo staging with weakly supervised region selection and dynamically decoded predictions"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "end to end learning of action detection from frame glimpses in videos"
    - "you only look once unified real time object detection"
    - "weakly supervised deep detection networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "depth adaptive deep neural network for semantic segmentation"):
  with members:
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "human pose estimation from depth images via inference embedded multi task learning"
    - "3d object reconstruction from hand object interactions"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "localization recall precision lrp a new performance metric for object detection"):
  with members:
    - "good features to correlate for visual tracking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "generalized optimal sub pattern assignment metric"
    - "towards high performance video object detection"
    - "focal loss for dense object detection"
    - "detect to track and track to detect"
    - "ssd single shot multibox detector"
    - "r fcn object detection via region based fully convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "assessing a mobile based deep learning model for plant disease surveillance"):
  with members:
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep learning for image based cassava disease detection"
    - "mask r cnn"
    - "using deep learning for image based plant disease detection"
    - "ssd single shot multibox detector"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (induced by source paper "end to end united video dehazing and detection"):
  with members:
    - "flow guided feature aggregation for video object detection"
    - "an all in one network for dehazing and beyond"
    - "coherent online video style transfer"
    - "dehazenet an end to end system for single image haze removal"
    - "object detection from video tubelets with convolutional neural networks"
    - "fast video classification via adaptive cascading of deep models"
    - "studying very low resolution recognition using deep networks"
    - "deep video deblurring"

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)