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: "surface normals in the wild"
  Abstract: We study the problem of single-image depth estimation for images in the wild. We collect human annotated surface normals and use them to train a neural network that directly predicts pixel-wise depth. We propose two novel loss functions for training with surface normal annotations. Experiments on NYU Depth and our own dataset demonstrate that our approach can significantly improve the quality of depth estimation in the wild.

Connected through hyperedge HE-1 (induced by source paper "360 o surface regression with a hyper sphere loss"):
  with members:
    - "corners for layout end to end layout recovery from 360 images"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "three for one and one for three flow segmentation and surface normals"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "learning to predict indoor illumination from a single image"

Connected through hyperedge HE-2 (induced by source paper "counterfactual depth from a single rgb image"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "deep depth completion of a single rgb d image"
    - "megadepth learning single view depth prediction from internet photos"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "unsupervised learning of depth and ego motion from video"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "semi supervised deep learning for monocular depth map prediction"
    - "ai2 thor an interactive 3d environment for visual ai"

Connected through hyperedge HE-3 (induced by source paper "deep surface normal estimation with hierarchical rgb d fusion"):
  with members:
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "depth aware cnn for rgb d segmentation"
    - "deep depth completion of a single rgb d image"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"
    - "noise2noise learning image restoration without clean data"
    - "deep graph laplacian regularization for robust denoising of real images"

Connected through hyperedge HE-4 (induced by source paper "thinking outside the box generation of unconstrained 3d room layouts"):
  with members:
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "weakly supervised object localization using things and stuff transfer"
    - "roomnet end to end room layout estimation"
    - "mask r cnn"
    - "stuffnet using stuff to improve object detection"
    - "deeper depth prediction with fully convolutional residual networks"
    - "pano2cad room layout from a single panorama image"

Connected through hyperedge HE-5 (induced by source paper "learning single image depth from videos using quality assessment networks"):
  with members:
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "soccer on your tabletop"
    - "megadepth learning single view depth prediction from internet photos"
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-6 (induced by source paper "groundnet monocular ground plane normal estimation with geometric consistency"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "real time correlation tracking via joint model compression and transfer"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised deep tracking"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep reinforcement learning for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"):
  with members:
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"
    - "free form image inpainting with gated convolution"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "lego learning edge with geometry all at once by watching videos"

Connected through hyperedge HE-8 (induced by source paper "learning to maintain natural image statistics"):
  with members:
    - "the perception distortion tradeoff"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "the contextual loss for image transformation with non aligned data"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "deep image prior"
    - "enhanced deep residual networks for single image super resolution"
    - "zero shot super resolution using deep internal learning"
    - "pixelnet representation of the pixels by the pixels and for the pixels"

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)