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: "relations among some low rank subspace recovery models"
  Abstract: Recovering intrinsic low dimensional subspaces from data distributed on them is a key preprocessing step to many applications. In recent years, there has been a lot of work that models subspace recovery as low rank minimization problems. We find that some representative models, such as Robust Principal Component Analysis (R-PCA), Robust Low Rank Representation (R-LRR), and Robust Latent Low Rank Representation (R-LatLRR), are actually deeply connected. More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations. Since R-PCA is the simplest, our discovery makes it the center of low rank subspace recovery models. Our work has two important implications. First, R-PCA has a solid theoretical foundation. Under certain conditions, we could find better solutions to these low rank models at overwhelming probabilities, although these models are non-convex. Second, we can obtain significantly faster algorithms for these models by solving R-PCA first. The computation cost can be further cut by applying low complexity randomized algorithms, e.g., our novel $\ell_{2,1}$ filtering algorithm, to R-PCA. Experiments verify the advantages of our algorithms over other state-of-the-art ones that are based on the alternating direction method.

Connected through hyperedge HE-1 (induced by source paper "testing matrix rank optimally"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "matrix completion and related problems via strong duality"
    - "embeddings of schatten norms with applications to data streams"
    - "on approximating functions of the singular values in a stream"
    - "the dictionary testing problem"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "sketching as a tool for numerical linear algebra"
    - "optimal algorithms for testing closeness of discrete distributions"

Connected through hyperedge HE-2 (induced by source paper "optimal sample complexity for matrix completion and related problems via 120001 s 2 regularization"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "how to escape saddle points efficiently"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "recovery guarantee of weighted low rank approximation via alternating minimization"
    - "a geometric analysis of phase retrieval"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"

Connected through hyperedge HE-3 (induced by source paper "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"):
  with members:
    - "connections between nuclear norm and frobenius norm based representations"
    - "noisy subspace clustering via matching pursuits"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "sssc am a unified framework for video co segmentation by structured sparse subspace clustering with appearance and motion features"
    - "convex sparse spectral clustering single view to multi view"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"

Connected through hyperedge HE-4 (induced by source paper "fast estimation of approximate matrix ranks using spectral densities"):
  with members:
    - "improved bounds on sample size for implicit matrix trace estimators"
    - "efficient estimation of eigenvalue counts in an interval"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "completing low rank matrices with corrupted samples from few coefficients in general basis"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "fixed rank representation for unsupervised visual learning"
    - "compressive principal component pursuit"
    - "algorithms and hardness for robust subspace recovery"
    - "recovering low rank matrices from few coefficients in any basis"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-6 (induced by source paper "noise tolerant life long matrix completion via adaptive sampling"):
  with members:
    - "high dimensional matched subspace detection when data are missing"
    - "robust pca via outlier pursuit"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "can dmd obtain a scene background in color"
    - "efficient background modeling based on sparse representation and outlier iterative removal"
    - "fast algorithms for robust pca via gradient descent"
    - "fast robust pca on graphs"
    - "approximated robust principal component analysis for improved general scene background subtraction"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

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)