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: "planercnn 3d plane detection and reconstruction from a single image"
  Abstract: This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs piecewise planar surfaces from a single RGB image. PlaneRCNN employs a variant of Mask R-CNN to detect planes with their plane parameters and segmentation masks. PlaneRCNN then jointly refines all the segmentation masks with a novel loss enforcing the consistency with a nearby view during training. The paper also presents a new benchmark with more fine-grained plane segmentations in the ground-truth, in which, PlaneRCNN outperforms existing state-of-the-art methods with significant margins in the plane detection, segmentation, and reconstruction metrics. PlaneRCNN makes an important step towards robust plane extraction, which would have an immediate impact on a wide range of applications including Robotics, Augmented Reality, and Virtual Reality.

Connected through hyperedge HE-1 (induced by source paper "from planes to corners multi purpose primitive detection in unorganized 3d point clouds"):
  with members:
    - "generic primitive detection in point clouds using novel minimal quadric fits"
    - "a minimalist approach to type agnostic detection of quadrics in point clouds"
    - "cad priors for accurate and flexible instance reconstruction"
    - "a large dataset of object scans"

Connected through hyperedge HE-2 (induced by source paper "general 3d room layout from a single view by render and compare"):
  with members:
    - "sharpnet fast and accurate recovery of occluding contours in monocular depth estimation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "in defense of classical image processing fast depth completion on the cpu"

Connected through hyperedge HE-3 (induced by source paper "object driven multi layer scene decomposition from a single image"):
  with members:
    - "peeking behind objects layered depth prediction from a single image"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep depth completion of a single rgb d image"
    - "layer structured 3d scene inference via view synthesis"
    - "stereo magnification learning view synthesis using multiplane images"
    - "adversarial semantic scene completion from a single depth image"
    - "segan segmenting and generating the invisible"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "floors are flat leveraging semantics for real time surface normal prediction"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"
    - "joint prediction of depths normals and surface curvature from rgb images using cnns"
    - "unsupervised learning of geometry with edge aware depth normal consistency"
    - "cross domain self supervised multi task feature learning using synthetic imagery"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "marr revisited 2d 3d alignment via surface normal prediction"

Connected through hyperedge HE-6 (induced by source paper "single image piece wise planar 3d reconstruction via associative embedding"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "recurrent pixel embedding for instance grouping"
    - "semantic instance segmentation via deep metric learning"
    - "semantic instance segmentation with a discriminative loss function"
    - "mask r cnn"
    - "semantic understanding of scenes through the ade20k dataset"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-7 (induced by source paper "end to end cad model retrieval and 9dof alignment in 3d scans"):
  with members:
    - "3d local features for direct pairwise registration"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "complete 3d scene parsing from an rgbd image"
    - "scancomplete large scale scene completion and semantic segmentation for 3d scans"

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)