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: "fast exact matrix completion with finite samples"
  Abstract: Matrix completion is the problem of recovering a low rank matrix by observing a small fraction of its entries. A series of recent works [KOM12,JNS13,HW14] have proposed fast non-convex optimization based iterative algorithms to solve this problem. However, the sample complexity in all these results is sub-optimal in its dependence on the rank, condition number and the desired accuracy. #R##N#In this paper, we present a fast iterative algorithm that solves the matrix completion problem by observing $O(nr^5 \log^3 n)$ entries, which is independent of the condition number and the desired accuracy. The run time of our algorithm is $O(nr^7\log^3 n\log 1/\epsilon)$ which is near linear in the dimension of the matrix. To the best of our knowledge, this is the first near linear time algorithm for exact matrix completion with finite sample complexity (i.e. independent of $\epsilon$). #R##N#Our algorithm is based on a well known projected gradient descent method, where the projection is onto the (non-convex) set of low rank matrices. There are two key ideas in our result: 1) our argument is based on a $\ell_{\infty}$ norm potential function (as opposed to the spectral norm) and provides a novel way to obtain perturbation bounds for it. 2) we prove and use a natural extension of the Davis-Kahan theorem to obtain perturbation bounds on the best low rank approximation of matrices with good eigen-gap. Both of these ideas may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "provable subspace tracking from missing data and matrix completion"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "subspace estimation from incomplete observations a high dimensional analysis"
    - "static and dynamic robust pca and matrix completion a review"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-2 (induced by source paper "fast exact matrix completion a unifying optimization framework"):
  with members:
    - "don t decay the learning rate increase the batch size"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "provable inductive matrix completion"
    - "clustering partially observed graphs via convex optimization"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from a few entries"

Connected through hyperedge HE-3 (induced by source paper "matrix completion and performance guarantees for single individual haplotyping"):
  with members:
    - "understanding alternating minimization for matrix completion"
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "fast robust and non convex subspace recovery"
    - "nonconvex matrix factorization from rank one measurements"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "recovery guarantees for one hidden layer neural networks"

Connected through hyperedge HE-5 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture"):
  with members:
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "simple efficient and neural algorithms for sparse coding"

Connected through hyperedge HE-6 (induced by source paper "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "non convex phase retrieval from stft measurements"
    - "a non convex blind calibration method for randomised sensing strategies"
    - "fast algorithms for robust pca via gradient descent"
    - "phase retrieval using gauss newton method"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-7 (induced by source paper "a geometric analysis of phase retrieval"):
  with members:
    - "non convex phase retrieval from stft measurements"
    - "phasemax convex phase retrieval via basis pursuit"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "phase retrieval via incremental truncated wirtinger flow"
    - "phaseless rcovery using gauss newton method"

Connected through hyperedge HE-8 (induced by source paper "finding a sparse vector in a subspace linear sparsity using alternating directions"):
  with members:
    - "a geometric analysis of phase retrieval"
    - "simple efficient and neural algorithms for sparse coding"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "tensor vs matrix methods robust tensor decomposition under block sparse perturbations"
    - "sum of squares lower bounds for sparse pca"
    - "speeding up sum of squares for tensor decomposition and planted sparse vectors"
    - "finding a low rank basis in a matrix subspace"
    - "dual principal component pursuit"

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.NA(Numerical Analysis)