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: "efficient matrix completion with gaussian models"
  Abstract: A general framework based on Gaussian models and a MAP-EM algorithm is introduced in this paper for solving matrix/table completion problems. The numerical experiments with the standard and challenging movie ratings data show that the proposed approach, based on probably one of the simplest probabilistic models, leads to the results in the same ballpark as the state-of-the-art, at a lower computational cost.

Connected through hyperedge HE-1 (induced by source paper "bilinear generalized approximate message passing part i derivation"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"
    - "expectation maximization gaussian mixture approximate message passing"
    - "matrix recipes for hard thresholding methods"
    - "iterative estimation of constrained rank one matrices in noise"
    - "iterative reconstruction of rank one matrices in noise"

Connected through hyperedge HE-2 (induced by source paper "exploring algorithmic limits of matrix rank minimization under affine constraints"):
  with members:
    - "generalized nonconvex nonsmooth low rank minimization"
    - "a tensor based method for missing traffic data completion"
    - "robust recovery of subspace structures by low rank representation"
    - "tilt transform invariant low rank textures"
    - "guaranteed rank minimization via singular value projection"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "online adaptive statistical compressed sensing of gaussian mixture models"):
  with members:
    - "statistical compressed sensing of gaussian mixture models"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-4 (induced by source paper "statistical compressed sensing of gaussian mixture models"):
  with members:
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"
    - "model based compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"):
  with members:
    - "image segmentation in video sequences a probabilistic approach"
    - "generalized principal component analysis gpca"
    - "statistical compressed sensing of gaussian mixture models"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "model based compressive sensing"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"

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)