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: "convolutional gated recurrent networks for video segmentation"
  Abstract: Semantic segmentation has recently witnessed major progress, where fully convolutional neural networks have shown to perform well. However, most of the previous work focused on improving single image segmentation. To our knowledge, no prior work has made use of temporal video information in a recurrent network. In this paper, we introduce a novel approach to implicitly utilize temporal data in videos for online semantic segmentation. The method relies on a fully convolutional network that is embedded into a gated recurrent architecture. This design receives a sequence of consecutive video frames and outputs the segmentation of the last frame. Convolutional gated recurrent networks are used for the recurrent part to preserve spatial connectivities in the image. Our proposed method can be applied in both online and batch segmentation. This architecture is tested for both binary and semantic video segmentation tasks. Experiments are conducted on the recent benchmarks in SegTrack V2, Davis, CityScapes, and Synthia. Using recurrent fully convolutional networks improved the baseline network performance in all of our experiments. Namely, 5% and 3% improvement of F-measure in SegTrack2 and Davis respectively, 5.7% improvement in mean IoU in Synthia and 3.5% improvement in categorical mean IoU in CityScapes. The performance of the RFCN network depends on its baseline fully convolutional network. Thus RFCN architecture can be seen as a method to improve its baseline segmentation network by exploiting spatiotemporal information in videos.

Connected through hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "model based reinforcement learning for atari"
    - "adversarial video generation on complex datasets"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"

Connected through hyperedge HE-2 (induced by source paper "implicit label augmentation on partially annotated clips via temporally adaptive features learning"):
  with members:
    - "panoptic segmentation"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-3 (induced by source paper "object parsing in sequences using coordconv gated recurrent networks"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "lstm pose machines"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "warpnet weakly supervised matching for single view reconstruction"

Connected through hyperedge HE-4 (induced by source paper "poseconvgru a monocular approach for visual ego motion estimation by learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "deepvo a deep learning approach for monocular visual odometry"
    - "direct sparse odometry"
    - "modelling uncertainty in deep learning for camera relocalization"

Connected through hyperedge HE-5 (induced by source paper "enhancing salient object segmentation through attention"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "learning video object segmentation with visual memory"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "shallow and deep convolutional networks for saliency prediction"
    - "recurrent attentional networks for saliency detection"
    - "holistically nested edge detection"

Connected through hyperedge HE-6 (induced by source paper "temporal convolution networks for real time abdominal fetal aorta analysis with ultrasound"):
  with members:
    - "automating carotid intima media thickness video interpretation with convolutional neural networks"
    - "temporal heartnet towards human level automatic analysis of fetal cardiac screening video"
    - "a survey on deep learning in medical image analysis"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "flow guided feature aggregation for video object detection"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "deformable convolutional networks"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "object detection from video tubelets with convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "differentiating objects by motion joint detection and tracking of small flying objects"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"
    - "flow guided feature aggregation for video object detection"
    - "perceptual generative adversarial networks for small object detection"
    - "detect to track and track to detect"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "object detection from video tubelets with convolutional neural networks"
    - "online multi target tracking using recurrent neural 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)