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: "an alternating direction algorithm for matrix completion with nonnegative factors"
  Abstract: This paper introduces an algorithm for the nonnegative matrix factorization-and-completion problem, which aims to find nonnegative low-rank matrices X and Y so that the product XY approximates a nonnegative data matrix M whose elements are partially known (to a certain accuracy). This problem aggregates two existing problems: (i) nonnegative matrix factorization where all entries of M are given, and (ii) low-rank matrix completion where nonnegativity is not required. By taking the advantages of both nonnegativity and low-rankness, one can generally obtain superior results than those of just using one of the two properties. We propose to solve the non-convex constrained least-squares problem using an algorithm based on the classical alternating direction augmented Lagrangian method. Preliminary convergence properties of the algorithm and numerical simulation results are presented. Compared to a recent algorithm for nonnegative matrix factorization, the proposed algorithm produces factorizations of similar quality using only about half of the matrix entries. On tasks of recovering incomplete grayscale and hyperspectral images, the proposed algorithm yields overall better qualities than those produced by two recent matrix-completion algorithms that do not exploit nonnegativity.

Connected through hyperedge HE-1 (induced by source paper "a group norm regularized lrr factorization model for spectral clustering"):
  with members:
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "analysis and improvement of low rank representation for subspace segmentation"

Connected through hyperedge HE-2 (induced by source paper "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"):
  with members:
    - "joint progressive learning from high dimensional data for multi label classification"
    - "dynamical spectral unmixing of multitemporal hyperspectral images"
    - "structured sparse method for hyperspectral unmixing"
    - "robust hyperspectral unmixing with correntropy based metric"

Connected through hyperedge HE-3 (induced by source paper "application of preconditioned alternating direction method of multipliers in depth from focal stack"):
  with members:
    - "parallel algorithms for constrained tensor factorization via alternating direction method of multipliers"
    - "variational depth from focus reconstruction"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"

Connected through hyperedge HE-4 (induced by source paper "iteratively linearized reweighted alternating direction method of multipliers for a class of nonconvex problems"):
  with members:
    - "proximal iteratively reweighted algorithm with multiple splitting for nonconvex sparsity optimization"

Connected through hyperedge HE-5 (induced by source paper "near optimal sample complexity for convex tensor completion"):
  with members:
    - "matrix completion via max norm constrained optimization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "exact tensor completion using t svd"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "nuclear norm of higher order tensors"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "concentration based guarantees for low rank matrix reconstruction"

Connected through hyperedge HE-6 (induced by source paper "a novel nonconvex approach to recover the low tubal rank tensor data when t svd meets pssv"):
  with members:
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "parallel matrix factorization for low rank tensor completion"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "generalized singular value thresholding"
    - "the power and arnoldi methods in an algebra of circulants"

Connected through hyperedge HE-7 (induced by source paper "clustering is semidefinitely not that hard nonnegative sdp for manifold disentangling"):
  with members:
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "on the tightness of an sdp relaxation of k means"
    - "on semidefinite relaxations for the block model"
    - "a biclustering framework for consensus problems"
    - "algorithms for learning kernels based on centered alignment"
    - "a new embedding quality assessment method for manifold learning"

Connected through hyperedge HE-8 (induced by source paper "global hard thresholding algorithms for joint sparse image representation and denoising"):
  with members:
    - "bethe admm for tree decomposition based parallel map inference"
    - "towards a mathematical theory of super resolution"
    - "decomposition methods for large scale lp decoding"
    - "guaranteed rank minimization via singular value projection"
    - "iterative hard thresholding for compressed sensing"

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)