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: "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"
  Abstract: In a plethora of applications dealing with inverse problems, e.g., image processing, social networks, compressive sensing, and biological data processing, the signal of interest is known to be structured in several ways at the same time. This premise has recently guided research into the innovative and meaningful idea of imposing multiple constraints on the unknown parameters involved in the problem under study. For instance, when dealing with problems whose unknown parameters form sparse and low-rank matrices, the adoption of suitably combined constraints imposing sparsity and low rankness is expected to yield substantially enhanced estimation results. In this paper, we address the spectral unmixing problem in hyperspectral images. Specifically, two novel unmixing algorithms are introduced in an attempt to exploit both spatial correlation and sparse representation of pixels lying in the homogeneous regions of hyperspectral images. To this end, a novel mixed penalty term is first defined consisting of the sum of the weighted   $\ell_{1}$   and the weighted nuclear norm of the abundance matrix corresponding to a small area of the image determined by a sliding square window. This penalty term is then used to regularize a conventional quadratic cost function and impose simultaneous sparsity and low rankness on the abundance matrix. The resulting regularized cost function is minimized by: 1) an   incremental proximal sparse and low-rank   unmixing algorithm; and 2) an algorithm based on the   alternating direction method of multipliers  . The effectiveness of the proposed algorithms is illustrated in experiments conducted both on simulated and real data.

Connected through hyperedge HE-1 (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"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "semi blind source separation via sparse representations and online dictionary learning"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "compressive principal component pursuit"
    - "low rank matrix recovery from errors and erasures"
    - "stable principal component pursuit"

Connected through hyperedge HE-2 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "efficient outlier removal for large scale global structure from motion"
    - "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"
    - "deterministic approximate methods for maximum consensus robust fitting"
    - "unified scalable equivalent formulations for schatten quasi norms"
    - "enhanced low rank matrix approximation"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-3 (induced by source paper "target based hyperspectral demixing via generalized robust pca"):
  with members:
    - "a dictionary based generalization of robust pca"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "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-4 (induced by source paper "alternating iteratively reweighted minimization algorithms for low rank matrix factorization"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "global optimality in low rank matrix optimization"
    - "unified scalable equivalent formulations for schatten quasi norms"
    - "guaranteed matrix completion via non convex factorization"
    - "generalized nonconvex nonsmooth low rank 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)