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: "low rank matrix completion with noisy observations a quantitative comparison"
  Abstract: We consider a problem of significant practical importance, namely, the reconstruction of a low-rank data matrix from a small subset of its entries. This problem appears in many areas such as collaborative filtering, computer vision and wireless sensor networks. In this paper, we focus on the matrix completion problem in the case when the observed samples are corrupted by noise. We compare the performance of three state-of-the-art matrix completion algorithms (OptSpace, ADMiRA and FPCA) on a single simulation platform and present numerical results. We show that in practice these efficient algorithms can be used to reconstruct real data matrices, as well as randomly generated matrices, accurately.

Connected through hyperedge HE-1 (induced by source paper "bilinear factor matrix norm minimization for robust pca algorithms and applications"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "guaranteed matrix completion via non convex factorization"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "robust principal component analysis on graphs"
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-2 (induced by source paper "a riemannian gossip approach to subspace learning on grassmann manifold"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "a survey on multi task learning"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian gossip approach to decentralized matrix completion"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a convex formulation for learning task relationships in multi task learning"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "a convex feature learning formulation for latent task structure discovery"

Connected through hyperedge HE-3 (induced by source paper "sequential matrix completion"):
  with members:
    - "learning to optimize via information directed sampling"
    - "learning to optimize via posterior sampling"
    - "linear bandits in high dimension and recommendation systems"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "further optimal regret bounds for thompson sampling"
    - "adadelta an adaptive learning rate method"
    - "a contextual bandit approach to personalized news article recommendation"
    - "matrix completion from a few entries"

Connected through hyperedge HE-4 (induced by source paper "scaled stochastic gradient descent for low rank matrix completion"):
  with members:
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a riemannian geometry for low rank matrix completion"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "practical matrix completion and corruption recovery using proximal alternating robust subspace minimization"):
  with members:
    - "a combinatorial algebraic approach for the identifiability of low rank matrix completion"
    - "stability of matrix factorization for collaborative filtering"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "online robust subspace tracking from partial information"
    - "low rank matrix recovery from errors and erasures"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "stable principal component pursuit"

Connected through hyperedge HE-6 (induced by source paper "signal recovery on graphs variation minimization"):
  with members:
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs frequency analysis"
    - "a spectral graph uncertainty principle"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "decentralized sparsity regularized rank minimization algorithms and applications"
    - "discrete signal processing on graphs"
    - "compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"

Connected through hyperedge HE-7 (induced by source paper "low rank modeling and its applications in image analysis"):
  with members:
    - "near optimal joint object matching via convex relaxation"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "compressive principal component pursuit"
    - "a riemannian geometry for low rank matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-8 (induced by source paper "a field guide to forward backward splitting with a fasta implementation"):
  with members:
    - "algorithms initializations and convergence for the nonnegative matrix factorization"
    - "fast approximations to structured sparse coding and applications to object classification"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "anti sparse coding for approximate nearest neighbor search"

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)