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: "online identification and tracking of subspaces from highly incomplete information"
  Abstract: This work presents GROUSE (Grassmanian Rank-One Update Subspace Estimation), an efficient online algorithm for tracking subspaces from highly incomplete observations. GROUSE requires only basic linear algebraic manipulations at each iteration, and each subspace update can be performed in linear time in the dimension of the subspace. The algorithm is derived by analyzing incremental gradient descent on the Grassmannian manifold of subspaces. With a slight modification, GROUSE can also be used as an online incremental algorithm for the matrix completion problem of imputing missing entries of a low-rank matrix. GROUSE performs exceptionally well in practice both in tracking subspaces and as an online algorithm for matrix completion.

Connected through hyperedge HE-1 (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"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-3 (induced by source paper "provable low rank phase retrieval and compressive pca"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "provable subspace tracking from missing data and matrix completion"
    - "phaseless pca low rank matrix recovery from column wise phaseless measurements"
    - "provable dynamic robust pca or robust subspace tracking"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "sparse phase retrieval via truncated amplitude flow"

Connected through hyperedge HE-4 (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-5 (induced by source paper "online structured sparsity based moving object detection from satellite videos"):
  with members:
    - "error bounded foreground and background modeling for moving object detection in satellite videos"
    - "dota a large scale dataset for object detection in aerial images"
    - "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
    - "a survey on object detection in optical remote sensing images"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "corola a sequential solution to moving object detection using low rank approximation"

Connected through hyperedge HE-6 (induced by source paper "provable dynamic robust pca or robust subspace tracking"):
  with members:
    - "static and dynamic robust pca and matrix completion a review"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "nearly optimal robust subspace tracking"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "nearly optimal robust matrix completion"
    - "online matrix completion and online robust pca"
    - "non convex robust pca"

Connected through hyperedge HE-7 (induced by source paper "glimps a greedy mixed integer approach for super robust matched subspace detection"):
  with members:
    - "deterministic hypothesis generation for robust fitting of multiple structures"
    - "deterministic consensus maximization with biconvex programming"
    - "dual principal component pursuit"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"
    - "high rank matrix completion and subspace clustering with missing data"
    - "high dimensional matched subspace detection when data are missing"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-8 (induced by source paper "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"):
  with members:
    - "riemannian stochastic quasi newton algorithm with variance reduction and its convergence analysis"
    - "riemannian stochastic variance reduced gradient on grassmann manifold"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "sdca without duality"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "manopt a matlab toolbox for optimization on manifolds"

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)