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: "recurrent fully convolutional networks for video segmentation"
  Abstract: Image segmentation is an important step in most visual tasks. While convolutional neural networks have shown to perform well on single image segmentation, to our knowledge, no study has been been done on leveraging recurrent gated architectures for video segmentation. Accordingly, we propose a novel method for online segmentation of video sequences that incorporates temporal data. The network is built from fully convolutional element and recurrent unit that works on a sliding window over the temporal data. We also introduce a novel convolutional gated recurrent unit that preserves the spatial information and reduces the parameters learned. Our method has the advantage that it can work in an online fashion instead of operating over the whole input batch of video frames. The network is tested on the change detection dataset, and proved to have 5.5\% improvement in F-measure over a plain fully convolutional network for per frame segmentation. It was also shown to have improvement of 1.4\% for the F-measure compared to our baseline network that we call FCN 12s.

Connected through hyperedge HE-1 (induced by source paper "on the texture bias for few shot cnn segmentation"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "a new local transformation module for few shot segmentation"
    - "meta learning initializations for image segmentation"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "feature weighting and boosting for few shot segmentation"
    - "texture bias of cnns limits few shot classification performance"
    - "adaptive masked proxies for few shot segmentation"
    - "panet few shot image semantic segmentation with prototype alignment"

Connected through hyperedge HE-2 (induced by source paper "unified image and video saliency modeling"):
  with members:
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "tased net temporally aggregating spatial encoder decoder network for video saliency detection"
    - "deep visual attention prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"

Connected through hyperedge HE-3 (induced by source paper "video object segmentation and tracking a survey"):
  with members:
    - "interactive video object segmentation in the wild"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "unsupervised video object segmentation for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "recurrent u net for resource constrained segmentation"):
  with members:
    - "fast scnn fast semantic segmentation network"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "group normalization"
    - "analysis of hand segmentation in the wild"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "beyond the pixel wise loss for topology aware delineation"

Connected through hyperedge HE-5 (induced by source paper "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"):
  with members:
    - "i can see clearly now image restoration via de raining"
    - "semantic segmentation of video sequences with convolutional lstms"
    - "semantic foggy scene understanding with synthetic data"
    - "optimal sensor data fusion architecture for object detection in adverse weather conditions"
    - "boosting lidar based semantic labeling by cross modal training data generation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "deep semantic classification for 3d lidar data"

Connected through hyperedge HE-6 (induced by source paper "semantic segmentation of video sequences with convolutional lstms"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "semantic object parsing with graph lstm"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"
    - "end to end instance segmentation and counting with recurrent attention"
    - "virtual worlds as proxy for multi object tracking analysis"

Connected through hyperedge HE-7 (induced by source paper "separable convolutional lstms for faster video segmentation"):
  with members:
    - "semantic segmentation of video sequences with convolutional lstms"
    - "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "factorization tricks for lstm networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"

Connected through hyperedge HE-8 (induced by source paper "using image priors to improve scene understanding"):
  with members:
    - "image generation from scene graphs"
    - "robust and precise vehicle localization based on multi sensor fusion in diverse city scenes"
    - "semantic video cnns through representation warping"
    - "perceptual losses for real time style transfer and super resolution"
    - "semantic video segmentation by gated recurrent flow propagation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "clockwork convnets for video semantic segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)