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 simpler approach to matrix completion"
  Abstract: This paper provides the best bounds to date on the number of randomly sampled entries required to reconstruct an unknown low rank matrix. These results improve on prior work by Candes and Recht, Candes and Tao, and Keshavan, Montanari, and Oh. The reconstruction is accomplished by minimizing the nuclear norm, or sum of the singular values, of the hidden matrix subject to agreement with the provided entries. If the underlying matrix satisfies a certain incoherence condition, then the number of entries required is equal to a quadratic logarithmic factor times the number of parameters in the singular value decomposition. The proof of this assertion is short, self contained, and uses very elementary analysis. The novel techniques herein are based on recent work in quantum information theory.

Connected through hyperedge HE-1 (induced by source paper "federated over the air subspace learning from incomplete data"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "provable subspace tracking from missing data and matrix completion"
    - "federated learning over wireless fading channels"
    - "federated pca with adaptive rank estimation"
    - "federated machine learning concept and applications"
    - "machine learning in the air"
    - "byzantine stochastic gradient descent"

Connected through hyperedge HE-2 (induced by source paper "provable meta learning of linear representations"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "online meta learning"
    - "multi task deep neural networks for natural language understanding"
    - "cloze driven pretraining of self attention networks"
    - "provable guarantees for gradient based meta learning"
    - "characterizing and avoiding negative transfer"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-3 (induced by source paper "matrix completion with prior subspace information via maximizing correlation"):
  with members:
    - "optimal weighted low rank matrix recovery with subspace prior information"
    - "recovery of structured signals with prior information via maximizing correlation"
    - "weighted matrix completion and recovery with prior subspace information"
    - "incoherence optimal matrix completion"
    - "provable inductive matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "clustering partially observed graphs via convex optimization"
    - "learning with the weighted trace norm under arbitrary sampling distributions"

Connected through hyperedge HE-4 (induced by source paper "recommendation on a budget column space recovery from partially observed entries with random or active sampling"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "streaming principal component analysis from incomplete data"
    - "matrix completion has no spurious local minimum"
    - "subspace learning with partial information"
    - "understanding alternating minimization for matrix completion"
    - "faster subset selection for matrices and applications"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-5 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems"):
  with members:
    - "fast stochastic algorithms for low rank and nonsmooth matrix problems"
    - "fast algorithms for robust pca via gradient descent"
    - "variance reduced and projection free stochastic optimization"
    - "matrix completion has no spurious local minimum"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "projection free online learning"
    - "phase retrieval via matrix completion"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "optimal adaptive matrix completion"):
  with members:
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "noise tolerant life long matrix completion via adaptive sampling"
    - "recovering low rank matrices from few coefficients in any basis"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "phase retrieval of low rank matrices by anchored regression"):
  with members:
    - "blind phaseless short time fourier transform recovery"
    - "blind deconvolutional phase retrieval via convex programming"
    - "blind gain and phase calibration via sparse spectral methods"
    - "solving equations of random convex functions via anchored regression"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "low rank phase retrieval"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration via linear least squares"

Connected through hyperedge HE-8 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion"):
  with members:
    - "phase retrieval without small ball probability assumptions"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "blind demixing and deconvolution at near optimal rate"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "low rank matrix recovery from clifford orbits"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex 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.IT(Information Theory)