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: "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
  Abstract: Given the noiseless superposition of a low-rank matrix plus the product of a known fat compression matrix times a sparse matrix, the goal of this paper is to establish deterministic conditions under which exact recovery of the low-rank and sparse components becomes possible. This fundamental identifiability issue arises with traffic anomaly detection in backbone networks, and subsumes compressed sensing as well as the timely low-rank plus sparse matrix recovery tasks encountered in matrix decomposition problems. Leveraging the ability of l1 and nuclear norms to recover sparse and low-rank matrices, a convex program is formulated to estimate the unknowns. Analysis and simulations confirm that the said convex program can recover the unknowns for sufficiently low-rank and sparse enough components, along with a compression matrix possessing an isometry property when restricted to operate on sparse vectors. When the low-rank, sparse, and compression matrices are drawn from certain random ensembles, it is established that exact recovery is possible with high probability. First-order algorithms are developed to solve the nonsmooth convex optimization problem with provable iteration complexity guarantees. Insightful tests with synthetic and real network data corroborate the effectiveness of the novel approach in unveiling traffic anomalies across flows and time, and its ability to outperform existing alternatives.

Connected through hyperedge HE-1 (induced by source paper "robust subspace clustering with compressed data"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "automatic subspace learning via principal coefficients embedding"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-2 (induced by source paper "a dictionary based generalization of robust pca part ii applications to hyperspectral demixing"):
  with members:
    - "target based hyperspectral demixing via generalized robust pca"
    - "a dictionary based generalization of robust pca"
    - "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "semi blind source separation via sparse representations and online dictionary learning"
    - "compressive principal component pursuit"
    - "low rank matrix recovery from errors and erasures"
    - "stable principal component pursuit"

Connected through hyperedge HE-3 (induced by source paper "a dictionary based generalization of robust pca part i study of theoretical properties"):
  with members:
    - "target based hyperspectral demixing via generalized robust pca"
    - "a dictionary based generalization of robust pca"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "compressive principal component pursuit"
    - "low rank matrix recovery from errors and erasures"
    - "stable principal component pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "static and dynamic robust pca and matrix completion a review"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "an overview of robust subspace recovery"
    - "video denoising via dynamic video layering"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"

Connected through hyperedge HE-5 (induced by source paper "static and dynamic robust pca via low rank sparse matrix decomposition a review"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "video denoising via dynamic video layering"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-6 (induced by source paper "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "an overview of robust subspace recovery"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "robust online matrix factorization for dynamic background subtraction"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "nearly optimal robust matrix completion"

Connected through hyperedge HE-7 (induced by source paper "target based hyperspectral demixing via generalized robust pca"):
  with members:
    - "a dictionary based generalization of robust pca"
    - "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "compressive principal component pursuit"
    - "low rank matrix recovery from errors and erasures"
    - "stable principal component pursuit"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "a parallel best response algorithm with exact line search for nonconvex sparsity regularized rank minimization"):
  with members:
    - "block and rank sparse recovery for direction finding in partly calibrated arrays"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"

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)