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: "dula net a dual projection network for estimating room layouts from a single rgb panorama"
  Abstract: We present a deep learning framework, called DuLa-Net, to predict Manhattan-world 3D room layouts from a single RGB panorama. To achieve better prediction accuracy, our method leverages two projections of the panorama at once, namely the equirectangular panorama-view and the perspective ceiling-view, that each contains different clues about the room layouts. Our network architecture consists of two encoder-decoder branches for analyzing each of the two views. In addition, a novel feature fusion structure is proposed to connect the two branches, which are then jointly trained to predict the 2D floor plans and layout heights. To learn more complex room layouts, we introduce the Realtor360 dataset that contains panoramas of Manhattan-world room layouts with different numbers of corners. Experimental results show that our work outperforms recent state-of-the-art in prediction accuracy and performance, especially in the rooms with non-cuboid layouts.

Connected through hyperedge HE-1 (induced by source paper "the direction aware learnable additive kernels and the adversarial network for deep floor plan recognition"):
  with members:
    - "deep floor plan recognition using a multi task network with room boundary guided attention"
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "generative face completion"

Connected through hyperedge HE-2 (induced by source paper "structured3d a large photo realistic dataset for structured 3d modeling"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "3d manhattan room layout reconstruction from a single 360 image"
    - "single image piece wise planar 3d reconstruction via associative embedding"
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "learning to reconstruct 3d manhattan wireframes from a single image"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "learning to adapt structured output space for semantic segmentation"
    - "planenet piece wise planar reconstruction from a single rgb image"

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"
    - "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"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-4 (induced by source paper "flat2layout flat representation for estimating layout of general room types"):
  with members:
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "robot localization in floor plans using a room layout edge extraction network"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "panoroom from the sphere to the 3d layout"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "joint layout estimation and global multi view registration for indoor reconstruction"
    - "roomnet end to end room layout estimation"

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"
    - "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"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-6 (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"
    - "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-7 (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"
    - "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"
    - "pano2cad room layout from a single panorama image"

Connected through hyperedge HE-8 (induced by source paper "deep floor plan recognition using a multi task network with room boundary guided attention"):
  with members:
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "deeply supervised salient object detection with short connections"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "richer convolutional features for edge detection"
    - "roomnet end to end room layout estimation"
    - "pyramid scene parsing network"
    - "very deep convolutional networks for large scale image recognition"

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)