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: "set an algorithm for consistent matrix completion"
  Abstract: A new algorithm, termed subspace evolution and transfer (SET), is proposed for solving the consistent matrix completion problem. In this setting, one is given a subset of the entries of a low-rank matrix, and asked to find one low-rank matrix consistent with the given observations. We show that this problem can be solved by searching for a column space that matches the observations. The corresponding algorithm consists of two parts -- subspace evolution and subspace transfer. In the evolution part, we use a line search procedure to refine the column space. However, line search is not guaranteed to converge, as there may exist barriers along the search path that prevent the algorithm from reaching a global optimum. To address this problem, in the transfer part, we design mechanisms to detect barriers and transfer the estimated column space from one side of the barrier to the another. The SET algorithm exhibits excellent empirical performance for very low-rank matrices.

Connected through hyperedge HE-1 (induced by source paper "low rank matrix completion a contemporary survey"):
  with members:
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "overview of full dimension mimo in lte advanced pro"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "poisson matrix recovery and completion"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "performance bounds for expander based compressed sensing in poisson noise"
    - "how well can we estimate a sparse vector"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "compressed sensing performance bounds under poisson noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"

Connected through hyperedge HE-3 (induced by source paper "forward backward greedy algorithms for atomic norm regularization"):
  with members:
    - "convexity in source separation models geometry and algorithms"
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "convex relaxations of structured matrix factorizations"
    - "solving oscar regularization problems by proximal splitting algorithms"
    - "tensor decompositions for learning latent variable models"
    - "block coordinate frank wolfe optimization for structural svms"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "orthogonal matching pursuit with replacement"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "a tensor based method for missing traffic data completion"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "robust principal component analysis exact recovery of corrupted low rank matrices"
    - "the power of convex relaxation near optimal matrix completion"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-7 (induced by source paper "rank aggregation via nuclear norm minimization"):
  with members:
    - "a tensor based method for missing traffic data completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "exact matrix completion via convex optimization"
    - "iterative filtering for a dynamical reputation system"

Connected through hyperedge HE-8 (induced by source paper "a geometric approach to low rank matrix completion"):
  with members:
    - "online identification and tracking of subspaces from highly incomplete information"
    - "the power of convex relaxation near optimal matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from a few entries"
    - "matrix completion with noise"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

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)