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 principal component analysis"
  Abstract: This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.

Connected through hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "nonconvex robust low rank matrix recovery"
    - "dual principal component pursuit probability analysis and efficient algorithms"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"

Connected through hyperedge HE-2 (induced by source paper "moving objects detection with a moving camera a comprehensive review"):
  with members:
    - "deeppbm deep probabilistic background model estimation from video sequences"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "combining background subtraction algorithms with convolutional neural network"
    - "background subtraction in real applications challenges current models and future directions"
    - "learning multi scale features for foreground segmentation"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "are object detection assessment criteria ready for maritime computer vision"

Connected through hyperedge HE-3 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection"):
  with members:
    - "deep semi supervised anomaly detection"
    - "one class convolutional neural network"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "latent space autoregression for novelty detection"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification"):
  with members:
    - "deep semi supervised anomaly detection"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficient gan based anomaly detection"
    - "deep anomaly detection using geometric transformations"
    - "escaping from collapsing modes in a constrained space"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "ell_0 motivated low rank sparse subspace clustering"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "multi view low rank sparse subspace clustering"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "enhanced low rank matrix approximation"
    - "improving compressed sensing with the diamond norm"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "generalized nonconvex nonsmooth low rank minimization"

Connected through hyperedge HE-6 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems"):
  with members:
    - "fast stochastic algorithms for low rank and nonsmooth matrix problems"
    - "fast algorithms for robust pca via gradient descent"
    - "variance reduced and projection free stochastic optimization"
    - "matrix completion has no spurious local minimum"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "projection free online learning"
    - "phase retrieval via matrix completion"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "g lbm generative low dimensional background model estimation from video sequences"):
  with members:
    - "deeppbm deep probabilistic background model estimation from video sequences"
    - "background subtraction in real applications challenges current models and future directions"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "a robust background initialization algorithm with superpixel motion detection"
    - "neural collaborative filtering"
    - "background subtraction via fast robust matrix completion"
    - "learning features by watching objects move"

Connected through hyperedge HE-8 (induced by source paper "on learning vehicle detection in satellite video"):
  with members:
    - "clustered object detection in aerial images"
    - "error bounded foreground and background modeling for moving object detection in satellite videos"
    - "multi cue vehicle detection for semantic video compression in georegistered aerial videos"
    - "yolov3 an incremental improvement"
    - "detecting tiny moving vehicles in satellite videos"
    - "clusternet detecting small objects in large scenes by exploiting spatio temporal information"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "faster r cnn towards real time object detection with region proposal networks"

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.IT(Information Theory)