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: "pano2cad room layout from a single panorama image"
  Abstract: This paper presents a method of estimating the geometry of a room and the 3D pose of objects from a single 360-degree panorama image. Assuming Manhattan World geometry, we formulate the task as a Bayesian inference problem in which we estimate positions and orientations of walls and objects. The method combines surface normal estimation, 2D object detection and 3D object pose estimation. Quantitative results are presented on a dataset of synthetically generated 3D rooms containing objects, as well as on a subset of hand-labeled images from the public SUN360 dataset.

Connected through hyperedge HE-1 (induced by source paper "cross view semantic segmentation for sensing surroundings"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "high resolution representations for labeling pixels and regions"
    - "learning exploration policies for navigation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "learning to adapt structured output space for semantic segmentation"
    - "cross view image synthesis using conditional gans"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "visual memory for robust path following"

Connected through hyperedge HE-2 (induced by source paper "pano popups indoor 3d reconstruction with a plane aware network"):
  with members:
    - "convolutions on spherical images"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "megadepth learning single view depth prediction from internet photos"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "joint prediction of depths normals and surface curvature from rgb images using cnns"

Connected through hyperedge HE-3 (induced by source paper "corners for layout end to end layout recovery from 360 images"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "omnidrl robust pedestrian detection using deep reinforcement learning on omnidirectional cameras"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"
    - "panoroom from the sphere to the 3d layout"
    - "layouts from panoramic images with geometry and deep learning"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "thinking outside the box generation of unconstrained 3d room layouts"):
  with members:
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "weakly supervised object localization using things and stuff transfer"
    - "roomnet end to end room layout estimation"
    - "surface normals in the wild"
    - "mask r cnn"
    - "stuffnet using stuff to improve object detection"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-5 (induced by source paper "3d manhattan room layout reconstruction from a single 360 image"):
  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"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "complete 3d scene parsing from an rgbd image"
    - "roomnet end to end room layout estimation"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-6 (induced by source paper "horizonnet learning room layout with 1d representation and pano stretch data augmentation"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"
    - "panoroom from the sphere to the 3d layout"
    - "layouts from panoramic images with geometry and deep learning"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "roomnet end to end room layout estimation"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-7 (induced by source paper "omnidepth dense depth estimation for indoors spherical panoramas"):
  with members:
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "eliminating the blind spot adapting 3d object detection and monocular depth estimation to 360 deg panoramic imagery"):
  with members:
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "cycada cycle consistent adversarial domain adaptation"
    - "carla an open urban driving simulator"
    - "unsupervised learning of depth and ego motion from video"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "adversarial discriminative domain adaptation"
    - "learning spherical convolution for fast features from 360 deg imagery"

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)