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: "robust low rank matrix factorization under general mixture noise distributions"
  Abstract: Many computer vision problems can be posed as learning a low-dimensional subspace from high-dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problems using    $L_{1}$   -norm and    $L_{2}$   -norm losses, which mainly deal with the Laplace and Gaussian noises, respectively. To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as mixture of exponential power (MoEP) distributions and then proposes a penalized MoEP (PMoEP) model by combining the penalized likelihood method with MoEP distributions. Such setting facilitates the learned LRMF model capable of automatically fitting the real noise through MoEP distributions. Each component in this mixture distribution is adapted from a series of preliminary super- or sub-Gaussian candidates. Moreover, by facilitating the local continuity of noise components, we embed Markov random field into the PMoEP model and then propose the PMoEP-MRF model. A generalized expectation maximization (GEM) algorithm and a variational GEM algorithm are designed to infer all parameters involved in the proposed PMoEP and the PMoEP-MRF model, respectively. The superiority of our methods is demonstrated by extensive experiments on synthetic data, face modeling, hyperspectral image denoising, and background subtraction.

Connected through hyperedge HE-1 (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"
    - "multi scale context aggregation by dilated convolutions"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-2 (induced by source paper "collaborative filtering and multi label classification with matrix factorization"):
  with members:
    - "cnn rnn a unified framework for multi label image classification"
    - "scalable multilabel prediction via randomized methods"
    - "large scale multi label learning with missing labels"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "the landmark selection method for multiple output prediction"
    - "multi label prediction via compressed sensing"
    - "a tutorial on spectral clustering"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-3 (induced by source paper "non local meets global an integrated paradigm for hyperspectral denoising"):
  with members:
    - "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"
    - "nonlocal low rank tensor factor analysis for image restoration"
    - "taking human out of learning applications a survey on automated machine learning"
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "learning deep cnn denoiser prior for image restoration"

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)