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: "sweepnet wide baseline omnidirectional depth estimation"
  Abstract: Omnidirectional depth sensing has its advantage over the conventional stereo systems since it enables us to recognize the objects of interest in all directions without any blind regions. In this paper, we propose a novel wide-baseline omnidirectional stereo algorithm which computes the dense depth estimate from the fisheye images using a deep convolutional neural network. The capture system consists of multiple cameras mounted on a wide-baseline rig with ultra-wide field of view (FOV) lenses, and we present the calibration algorithm for the extrinsic parameters based on the bundle adjustment. Instead of estimating depth maps from multiple sets of rectified images and stitching them, our approach directly generates one dense omnidirectional depth map with full 360° coverage at the rig global coordinate system. To this end, the proposed neural network is designed to output the cost volume from the warped images in the sphere sweeping method, and the final depth map is estimated by taking the minimum cost indices of the aggregated cost volume by SGM. For training the deep neural network and testing the entire system, realistic synthetic urban datasets are rendered using Blender. The experiments using the synthetic and real-world datasets show that our algorithm outperforms the conventional depth estimation methods and generate highly accurate depth maps.

Connected through hyperedge HE-1 (induced by source paper "monocular 3d object detection in cylindrical images from fisheye cameras"):
  with members:
    - "spherical view synthesis for self supervised 360 depth estimation"
    - "disentangling monocular 3d object detection"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "unsupervised domain adaptation through self supervision"
    - "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving"
    - "unsupervised learning of depth and ego motion from cylindrical panoramic video"
    - "reprojection r cnn a fast and accurate object detector for 360 images"

Connected through hyperedge HE-2 (induced by source paper "omnislam omnidirectional localization and dense mapping for wide baseline multi camera systems"):
  with members:
    - "tightly coupled 3d lidar inertial odometry and mapping"
    - "omnimvs end to end learning for omnidirectional stereo matching"
    - "ga net guided aggregation net for end to end stereo matching"
    - "rovo robust omnidirectional visual odometry for wide baseline wide fov camera systems"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "real time dense mapping for self driving vehicles using fisheye cameras"
    - "omnidirectional dso direct sparse odometry with fisheye cameras"
    - "pyramid stereo matching network"

Connected through hyperedge HE-3 (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"
    - "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"
    - "kernel transformer networks for compact spherical convolution"
    - "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-4 (induced by source paper "omnimvs end to end learning for omnidirectional stereo matching"):
  with members:
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "pyramid stereo matching network"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "playing for benchmarks"
    - "semantic scene completion from a single depth image"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "shapenet an information rich 3d model repository"

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)