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: "a dictionary based generalization of robust pca"
  Abstract: We analyze the decomposition of a data matrix, assumed to be a superposition of a low-rank component and a component which is sparse in a known dictionary, using a convex demixing method. We provide a unified analysis, encompassing both undercomplete and overcomplete dictionary cases, and show that the constituent components can be successfully recovered under some relatively mild assumptions up to a certain global sparsity level. Further, we corroborate our theoretical results by presenting empirical evaluations in terms of phase transitions in rank and sparsity for various dictionary sizes.

Connected through hyperedge HE-1 (induced by source paper "noodl provable online dictionary learning and sparse coding"):
  with members:
    - "a dictionary based generalization of robust pca part ii applications to hyperspectral demixing"
    - "a dictionary based generalization of robust pca part i study of theoretical properties"
    - "target based hyperspectral demixing via generalized robust pca"
    - "stochastic variance reduced optimization for nonconvex sparse learning"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "simple efficient and neural algorithms for sparse coding"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "dictionary learning and tensor decomposition via the sum of squares method"

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"
    - "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"
    - "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-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"
    - "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 principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "target based hyperspectral demixing via generalized robust pca"):
  with members:
    - "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"
    - "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"

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.LG(Machine Learning)