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: "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
  Abstract: Understanding the surrounding environment of the vehicle is still one of the challenges for autonomous driving. This paper addresses 360-degree road scene semantic segmentation using surround view cameras, which are widely equipped in existing production cars. First, in order to address large distortion problem in the fisheye images, Restricted Deformable Convolution (RDC) is proposed for semantic segmentation, which can effectively model geometric transformations by learning the shapes of convolutional filters conditioned on the input feature map. Second, in order to obtain a large-scale training set of surround view images, a novel method called zoom augmentation is proposed to transform conventional images to fisheye images. Finally, an RDC based semantic segmentation model is built; the model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with transformed images. Experiments demonstrate the effectiveness of the RDC to handle images with large distortions, and that the proposed approach shows a good performance using surround view cameras with the help of the transformed images.

Connected through hyperedge HE-1 (induced by source paper "universal semantic segmentation for fisheye urban driving images"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "ds pass detail sensitive panoramic annular semantic segmentation through swaftnet for surrounding sensing"
    - "rgb and lidar fusion based 3d semantic segmentation for autonomous driving"
    - "scene understanding networks for autonomous driving based on around view monitoring system"
    - "deformable convolutional networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving"):
  with members:
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "3d packing for self supervised monocular depth estimation"
    - "rethinking imagenet pre training"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "group normalization"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-3 (induced by source paper "ds pass detail sensitive panoramic annular semantic segmentation through swaftnet for surrounding sensing"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "cross view semantic segmentation for sensing surroundings"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "orientation aware semantic segmentation on icosahedron spheres"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "a comparative study of high recall real time semantic segmentation based on swift factorized network"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "weakly supervised silhouette based semantic change detection"

Connected through hyperedge HE-4 (induced by source paper "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "gff gated fully fusion for semantic segmentation"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "long term visual localization using semantically segmented images"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "in defense of classical image processing fast depth completion on the cpu"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "semantic segmentation of panoramic images using a synthetic dataset"):
  with members:
    - "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "unsupervised learning of depth and ego motion from panoramic video"
    - "importance aware semantic segmentation with efficient pyramidal context network for navigational assistant systems"
    - "orientation aware semantic segmentation on icosahedron spheres"
    - "adapting semantic segmentation models for changes in illumination and camera perspective"
    - "a comparative study of high recall real time semantic segmentation based on swift factorized network"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"

Connected through hyperedge HE-6 (induced by source paper "omnidepth dense depth estimation for indoors spherical panoramas"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"
    - "light weight pixel context encoders for image inpainting"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
    - "learning high dynamic range from outdoor panoramas"
    - "active convolution learning the shape of convolution for image classification"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-7 (induced by source paper "cream condensed real time models for depth prediction using convolutional neural networks"):
  with members:
    - "fusion of stereo and still monocular depth estimates in a self supervised learning context"
    - "eng end to end neural geometry for robust depth and pose estimation using cnns"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "joint prediction of depths normals and surface curvature from rgb images using cnns"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "efficient semantic segmentation for visual bird s eye view interpretation"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "channel pruning for accelerating very deep neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "lcnn lookup based convolutional neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "rethinking the inception architecture for computer vision"

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)