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: "roomnet end to end room layout estimation"
  Abstract: This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of floor, walls, ceiling to produce layout hypotheses, followed by an iterative optimization step to rank these hypotheses. In contrast, we adopt a more direct formulation of this problem as one of estimating an ordered set of room layout keypoints. The room layout and the corresponding segmentation is completely specified given the locations of these ordered keypoints. We predict the locations of the room layout keypoints using RoomNet, an end-to-end trainable encoder-decoder network. On the challenging benchmark datasets Hedau and LSUN, we achieve state-of-the-art performance along with 200x to 600x speedup compared to the most recent work. Additionally, we present optional extensions to the RoomNet architecture such as including recurrent computations and memory units to refine the keypoint locations under the same parametric capacity.

Connected through hyperedge HE-1 (induced by source paper "shallow2deep indoor scene modeling by single image understanding"):
  with members:
    - "learning shape priors for single view 3d completion and reconstruction"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "a simple neural network module for relational reasoning"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (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-4 (induced by source paper "clouds of oriented gradients for 3d detection of objects surfaces and indoor scene layouts"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "semantic scene completion from a single depth image"
    - "3d object proposals using stereo imagery for accurate object class detection"
    - "3d bounding box estimation using deep learning and geometry"

Connected through hyperedge HE-5 (induced by source paper "zero shot task transfer"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "latent ransac"
    - "bayesian model agnostic meta learning"
    - "taskonomy disentangling task transfer learning"
    - "adversarial data programming using gans to relax the bottleneck of curated labeled data"
    - "domain adaptation for visual applications a comprehensive survey"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"

Connected through hyperedge HE-7 (induced by source paper "deepperimeter indoor boundary estimation from posed monocular sequences"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "estimating depth from rgb and sparse sensing"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-8 (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"

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)