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: "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
  Abstract: We present a new approach to the problem of estimating the 3D room layout from a single panoramic image. We represent room layout as three 1D vectors that encode, at each image column, the boundary positions of floor-wall and ceiling-wall, and the existence of wall-wall boundary. The proposed network, HorizonNet, trained for predicting 1D layout, outperforms previous state-of-the-art approaches. The designed post-processing procedure for recovering 3D room layouts from 1D predictions can automatically infer the room shape with low computation cost - it takes less than 20ms for a panorama image while prior works might need dozens of seconds. We also propose Pano Stretch Data Augmentation, which can diversify panorama data and be applied to other panorama-related learning tasks. Due to the limited data available for non-cuboid layout, we relabel 65 general layout from the current dataset for finetuning. Our approach shows good performance on general layouts by qualitative results and cross-validation.

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"
    - "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"
    - "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"
    - "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 "coco gan generation by parts via conditional coordinating"):
  with members:
    - "cgans with projection discriminator"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "spectral normalization for generative adversarial networks"
    - "painting outside the box image outpainting with gans"
    - "escaping from collapsing modes in a constrained space"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-3 (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"
    - "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"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"
    - "planenet piece wise planar reconstruction from a single rgb image"

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"
    - "robot localization in floor plans using a room layout edge extraction network"
    - "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"
    - "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 "what s in my room object recognition on indoor panoramic images"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "layouts from panoramic images with geometry and deep learning"
    - "blitznet a real time deep network for scene understanding"
    - "mask r cnn"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "semantic scene completion from a single depth image"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-6 (induced by source paper "3d manhattan room layout reconstruction from a single 360 image"):
  with members:
    - "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"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-7 (induced by source paper "360sd net 360 stereo depth estimation with learnable cost volume"):
  with members:
    - "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"
    - "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-8 (induced by source paper "deep floor plan recognition using a multi task network with room boundary guided attention"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "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"
    - "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)