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: "tilt transform invariant low rank textures"
  Abstract: In this paper, we show how to efficiently and effectively extract a class of "low-rank textures" in a 3D scene from 2D images despite significant corruptions and warping. The low-rank textures capture geometrically meaningful structures in an image, which encompass conventional local features such as edges and corners as well as all kinds of regular, symmetric patterns ubiquitous in urban environments and man-made objects. Our approach to finding these low-rank textures leverages the recent breakthroughs in convex optimization that enable robust recovery of a high-dimensional low-rank matrix despite gross sparse errors. In the case of planar regions with significant affine or projective deformation, our method can accurately recover both the intrinsic low-rank texture and the precise domain transformation, and hence the 3D geometry and appearance of the planar regions. Extensive experimental results demonstrate that this new technique works effectively for many regular and near-regular patterns or objects that are approximately low-rank, such as symmetrical patterns, building facades, printed texts, and human faces.

Connected through hyperedge HE-1 (induced by source paper "learning to reconstruct 3d manhattan wireframes from a single image"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "megadepth learning single view depth prediction from internet photos"
    - "point linking network for object detection"
    - "semantic scene completion from a single depth image"
    - "stacked hourglass networks for human pose estimation"
    - "single image 3d interpreter network"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "video rain snow removal by transformed online multiscale convolutional sparse coding"):
  with members:
    - "robust video content alignment and compensation for clear vision through the rain"
    - "desnownet context aware deep network for snow removal"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "image de raining using a conditional generative adversarial network"
    - "robust online matrix factorization for dynamic background subtraction"
    - "deep joint rain detection and removal from a single image"
    - "robust low rank matrix factorization under general mixture noise distributions"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-3 (induced by source paper "minimal solvers for rectifying from radially distorted conjugate translations"):
  with members:
    - "minimal solvers for rectifying from radially distorted scales and change of scales"
    - "rectification from radially distorted scales"
    - "repeatability is not enough learning affine regions via discriminability"
    - "radially distorted conjugate translations"
    - "coplanar repeats by energy minimization"

Connected through hyperedge HE-4 (induced by source paper "minimal solvers for rectifying from radially distorted scales and change of scales"):
  with members:
    - "beyond gr obner bases basis selection for minimal solvers"
    - "repeatability is not enough learning affine regions via discriminability"
    - "radially distorted conjugate translations"
    - "coplanar repeats by energy minimization"

Connected through hyperedge HE-5 (induced by source paper "masked rpca sparse and low rank decomposition under overlaying model and application to moving object detection"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "matrix completion from noisy entries"
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "a comprehensive survey for low rank regularization"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "side information for face completion a robust pca approach"
    - "fast and accurate tensor completion with tensor trains a system identification approach"
    - "discriminative block diagonal representation learning for image recognition"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "large scale low rank matrix learning with nonconvex regularizers"

Connected through hyperedge HE-7 (induced by source paper "bilinear factor matrix norm minimization for robust pca algorithms and applications"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "guaranteed matrix completion via non convex factorization"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "robust principal component analysis on graphs"
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-8 (induced by source paper "image segmentation using subspace representation and sparse decomposition"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "an experimental study of deep convolutional features for iris recognition"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "robust computation of linear models by convex relaxation"
    - "learning deconvolution network for semantic segmentation"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "fully convolutional networks for semantic segmentation"
    - "stable image reconstruction using total variation minimization"

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)