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: "low rank modeling and its applications in image analysis"
  Abstract: Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing and bioinformatics. Recently, much progress has been made in theories, algorithms and applications of low-rank modeling, such as exact low-rank matrix recovery via convex programming and matrix completion applied to collaborative filtering. These advances have brought more and more attentions to this topic. In this paper, we review the recent advance of low-rank modeling, the state-of-the-art algorithms, and related applications in image analysis. We first give an overview to the concept of low-rank modeling and challenging problems in this area. Then, we summarize the models and algorithms for low-rank matrix recovery and illustrate their advantages and limitations with numerical experiments. Next, we introduce a few applications of low-rank modeling in the context of image analysis. Finally, we conclude this paper with some discussions.

Connected through hyperedge HE-1 (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-2 (induced by source paper "near optimal matrix recovery from random linear measurements"):
  with members:
    - "phase diagram of matrix compressed sensing"
    - "parametric bilinear generalized approximate message passing"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "sharp time data tradeoffs for linear inverse problems"
    - "bilinear generalized approximate message passing part i derivation"
    - "from denoising to compressed sensing"
    - "living on the edge phase transitions in convex programs with random data"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"

Connected through hyperedge HE-3 (induced by source paper "robust rotation synchronization via low rank and sparse matrix decomposition"):
  with members:
    - "a survey of structure from motion"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "spectral synchronization of multiple views in se 3"
    - "on computing the translations norm in the epipolar graph"
    - "practical matrix completion and corruption recovery using proximal alternating robust subspace minimization"
    - "a solution for multi alignment by transformation synchronisation"
    - "cramer rao bounds for synchronization of rotations"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-4 (induced by source paper "painless breakups efficient demixing of low rank matrices"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "convolutional dictionary learning through tensor factorization"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "sparse signal processing concepts for efficient 5g system design"
    - "convexity in source separation models geometry and algorithms"
    - "the achievable performance of convex demixing"
    - "blind deconvolution using convex programming"
    - "compressive principal component pursuit"

Connected through hyperedge HE-5 (induced by source paper "fast singular value shrinkage with chebyshev polynomial approximation based on signal sparsity"):
  with members:
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "an algorithm for improving non local means operators via low rank approximation"
    - "convexity in source separation models geometry and algorithms"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "non linear dimensionality regularizer for solving inverse problems"):
  with members:
    - "better feature tracking through subspace constraints"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "can dmd obtain a scene background in color"
    - "efficient background modeling based on sparse representation and outlier iterative removal"
    - "fast algorithms for robust pca via gradient descent"
    - "fast robust pca on graphs"
    - "approximated robust principal component analysis for improved general scene background subtraction"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

Connected through hyperedge HE-8 (induced by source paper "moving object detection in video using saliency map and subspace learning"):
  with members:
    - "inner and inter label propagation salient object detection in the wild"
    - "background subtraction via generalized fused lasso foreground modeling"
    - "corola a sequential solution to moving object detection using low rank approximation"
    - "generalized principal component analysis gpca"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "robust principal component analysis"

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)