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: "planenet piece wise planar reconstruction from a single rgb image"
  Abstract: This paper proposes a deep neural network (DNN) for piece-wise planar depthmap reconstruction from a single RGB image. While DNNs have brought remarkable progress to single-image depth prediction, piece-wise planar depthmap reconstruction requires a structured geometry representation, and has been a difficult task to master even for DNNs. The proposed end-to-end DNN learns to directly infer a set of plane parameters and corresponding plane segmentation masks from a single RGB image. We have generated more than 50,000 piece-wise planar depthmaps for training and testing from ScanNet, a large-scale RGBD video database. Our qualitative and quantitative evaluations demonstrate that the proposed approach outperforms baseline methods in terms of both plane segmentation and depth estimation accuracy. To the best of our knowledge, this paper presents the first end-to-end neural architecture for piece-wise planar reconstruction from a single RGB image. Code and data are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields"):
  with members:
    - "sharpnet fast and accurate recovery of occluding contours in monocular depth estimation"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "boundary cues for 3d object shape recovery"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "deep ordinal regression network for monocular depth estimation"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "evaluation of cnn based single image depth estimation methods"
    - "fast end to end trainable guided filter"

Connected through hyperedge HE-2 (induced by source paper "unsupervised high resolution depth learning from videos with dual networks"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning visual question answering by bootstrapping hard attention"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-3 (induced by source paper "multidepth single image depth estimation via multi task regression and classification"):
  with members:
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "deep attention based classification network for robust depth prediction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "learning monocular depth by distilling cross domain stereo networks"

Connected through hyperedge HE-4 (induced by source paper "uprightnet geometry aware camera orientation estimation from single images"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "groundnet segmentation aware monocular ground plane estimation with geometric consistency"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "deep depth completion of a single rgb d image"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"

Connected through hyperedge HE-5 (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-6 (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"
    - "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"
    - "roomnet end to end room layout estimation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-7 (induced by source paper "end to end wireframe parsing"):
  with members:
    - "bottom up object detection by grouping extreme and center points"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "cornernet detecting objects as paired keypoints"
    - "learning attraction field representation for robust line segment detection"
    - "deformable convolutional networks"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "stacked hourglass networks for human pose estimation"
    - "fully convolutional instance aware semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "analyzing the variety loss in the context of probabilistic trajectory prediction"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "inferring 3d shapes from image collections using adversarial networks"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"

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)