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: "kernel transformer networks for compact spherical convolution"
  Abstract: Ideally, 360{\deg} imagery could inherit the deep convolutional neural networks (CNNs) already trained with great success on perspective projection images. However, existing methods to transfer CNNs from perspective to spherical images introduce significant computational costs and/or degradations in accuracy. In this work, we present the Kernel Transformer Network (KTN). KTNs efficiently transfer convolution kernels from perspective images to the equirectangular projection of 360{\deg} images. Given a source CNN for perspective images as input, the KTN produces a function parameterized by a polar angle and kernel as output. Given a novel 360{\deg} image, that function in turn can compute convolutions for arbitrary layers and kernels as would the source CNN on the corresponding tangent plane projections. Distinct from all existing methods, KTNs allow model transfer: the same model can be applied to different source CNNs with the same base architecture. This enables application to multiple recognition tasks without re-training the KTN. Validating our approach with multiple source CNNs and datasets, we show that KTNs improve the state of the art for spherical convolution. KTNs successfully preserve the source CNN's accuracy, while offering transferability, scalability to typical image resolutions, and, in many cases, a substantially lower memory footprint.

Connected through hyperedge HE-1 (induced by source paper "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images"):
  with members:
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "underwater fish detection with weak multi domain supervision"
    - "datasets for face and object detection in fisheye images"
    - "spherical cnns on unstructured grids"
    - "spherical u net on cortical surfaces methods and applications"
    - "tagslam robust slam with fiducial markers"
    - "convolutions on spherical images"
    - "reprojection r cnn a fast and accurate object detector for 360 images"

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"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "spherical view synthesis for self supervised 360 depth estimation"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "single view stereo matching"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "learning structure from motion from motion"

Connected through hyperedge HE-4 (induced by source paper "woodscape a multi task multi camera fisheye dataset for autonomous driving"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "less is more culling the training set to improve robustness of deep neural networks"
    - "deep learning is robust to massive label noise"
    - "rtseg real time semantic segmentation comparative study"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-5 (induced by source paper "orientation aware semantic segmentation on icosahedron spheres"):
  with members:
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "spherephd applying cnns on a spherical polyhedron representation of 360 degree images"
    - "cubenet equivariance to 3d rotation and translation"
    - "oriented response networks"
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "reprojection r cnn a fast and accurate object detector for 360 images"):
  with members:
    - "yolov3 an incremental improvement"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "learning so 3 equivariant representations with spherical cnns"
    - "mask r cnn"
    - "unrealcv connecting computer vision to unreal engine"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-7 (induced by source paper "360sd net 360 stereo depth estimation with learnable cost volume"):
  with members:
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "sweepnet wide baseline omnidirectional depth estimation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "pyramid stereo matching network"
    - "minos multimodal indoor simulator for navigation in complex environments"

Connected through hyperedge HE-8 (induced by source paper "tangent images for mitigating spherical distortion"):
  with members:
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "orientation aware semantic segmentation on icosahedron spheres"
    - "mapped convolutions"
    - "convolutions on spherical images"
    - "learning so 3 equivariant representations with spherical cnns"
    - "convolutional networks for spherical signals"
    - "joint 2d 3d semantic data for indoor scene understanding"

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)