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: "matrix completion from a few entries"
  Abstract: Let M be a random (alpha n) x n matrix of rank r<<n, and assume that a uniformly random subset E of its entries is observed. We describe an efficient algorithm that reconstructs M from |E| = O(rn) observed entries with relative root mean square error RMSE <= C(rn/|E|)^0.5 . Further, if r=O(1), M can be reconstructed exactly from |E| = O(n log(n)) entries. These results apply beyond random matrices to general low-rank incoherent matrices. #R##N#This settles (in the case of bounded rank) a question left open by Candes and Recht and improves over the guarantees for their reconstruction algorithm. The complexity of our algorithm is O(|E|r log(n)), which opens the way to its use for massive data sets. In the process of proving these statements, we obtain a generalization of a celebrated result by Friedman-Kahn-Szemeredi and Feige-Ofek on the spectrum of sparse random matrices.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "online high rank matrix completion"):
  with members:
    - "guaranteed matrix completion via non convex factorization"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "online matrix completion and online robust pca"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "understanding alternating minimization for matrix completion"
    - "sparse subspace clustering algorithm theory and applications"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "community detection on mixture multi layer networks via regularized tensor decomposition"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "a statistical model for tensor pca"
    - "clustering with multi layer graphs a spectral perspective"
    - "most tensor problems are np hard"

Connected through hyperedge HE-5 (induced by source paper "revisiting landscape analysis in deep neural networks eliminating decreasing paths to infinity"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "elimination of all bad local minima in deep learning"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-6 (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-7 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "iterative collaborative filtering for sparse noisy tensor estimation"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "fundamental conditions for low cp rank tensor completion"
    - "exact tensor completion with sum of squares"
    - "on the optimization landscape of tensor decompositions"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"

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.LG(Machine Learning)