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: "cgnet a light weight context guided network for semantic segmentation"
  Abstract: The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuitable for mobile devices, while other small memory footprint models follow the spirit of classification network and ignore the inherent characteristic of semantic segmentation. To tackle this problem, we propose a novel Context Guided Network (CGNet), which is a light-weight and efficient network for semantic segmentation. We first propose the Context Guided (CG) block, which learns the joint feature of both local feature and surrounding context, and further improves the joint feature with the global context. Based on the CG block, we develop CGNet which captures contextual information in all stages of the network and is specially tailored for increasing segmentation accuracy. CGNet is also elaborately designed to reduce the number of parameters and save memory footprint. Under an equivalent number of parameters, the proposed CGNet significantly outperforms existing segmentation networks. Extensive experiments on Cityscapes and CamVid datasets verify the effectiveness of the proposed approach. Specifically, without any post-processing and multi-scale testing, the proposed CGNet achieves 64.8% mean IoU on Cityscapes with less than 0.5 M parameters. The source code for the complete system can be found at this https URL.

Connected through hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "linear context transform block"
    - "searching for mobilenetv3"
    - "a simple pooling based design for real time salient object detection"
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deeply supervised salient object detection with short connections"

Connected through hyperedge HE-2 (induced by source paper "few shot video to video synthesis"):
  with members:
    - "video generation from single semantic label map"
    - "pixel adaptive convolutional neural networks"
    - "dance dance generation motion transfer for internet videos"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "vid2game controllable characters extracted from real world videos"
    - "cgans with projection discriminator"
    - "video to video synthesis"

Connected through hyperedge HE-3 (induced by source paper "context integrated and feature refined network for lightweight object parsing"):
  with members:
    - "connnet a long range relation aware pixel connectivity network for salient segmentation"
    - "vortex pooling improving context representation in semantic segmentation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rtseg real time semantic segmentation comparative study"
    - "image super resolution using very deep residual channel attention networks"
    - "guided upsampling network for real time semantic segmentation"
    - "driving scene perception network real time joint detection depth estimation and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "consensus feature network for scene parsing"):
  with members:
    - "asymmetric non local neural networks for semantic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "adaptive context network for scene parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "esnet an efficient symmetric network for real time semantic segmentation"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "icnet for real time semantic segmentation on high resolution images"
    - "understanding convolution for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "lednet a lightweight encoder decoder network for real time semantic segmentation"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "feature pyramid encoding network for real time semantic segmentation"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid attention network for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "fddwnet a lightweight convolutional neural network for real time sementic segmentation"):
  with members:
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "fast scnn fast semantic segmentation network"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "dsnet for real time driving scene semantic segmentation"
    - "dual attention network for scene segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)