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 geometric approach to low rank matrix completion"
  Abstract: The low-rank matrix completion problem can be succinctly stated as follows: given a subset of the entries of a matrix, find a low-rank matrix consistent with the observations. While several low-complexity algorithms for matrix completion have been proposed so far, it remains an open problem to devise search procedures with provable performance guarantees for a broad class of matrix models. The standard approach to the problem, which involves the minimization of an objective function defined using the Frobenius metric, has inherent difficulties: the objective function is not continuous and the solution set is not closed. To address this problem, we consider an optimization procedure that searches for a column (or row) space that is geometrically consistent with the partial observations. The geometric objective function is continuous everywhere and the solution set is the closure of the solution set of the Frobenius metric. We also preclude the existence of local minimizers, and hence establish strong performance guarantees, for special completion scenarios, which do not require matrix incoherence or large matrix size.

Connected through hyperedge HE-1 (induced by source paper "a riemannian gossip approach to subspace learning on grassmann manifold"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "a survey on multi task learning"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian gossip approach to decentralized matrix completion"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a convex formulation for learning task relationships in multi task learning"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "a convex feature learning formulation for latent task structure discovery"

Connected through hyperedge HE-2 (induced by source paper "a riemannian gossip approach to decentralized matrix completion"):
  with members:
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "matrix completion from a few entries"

Connected through hyperedge HE-3 (induced by source paper "mimo mc radar a mimo radar approach based on matrix completion"):
  with members:
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "target estimation in colocated mimo radar via matrix completion"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "mimo radar using compressive sampling"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "fixed rank matrix factorizations and riemannian low rank optimization"):
  with members:
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a riemannian geometry for low rank matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "subspace evolution and transfer set for low rank matrix completion"
    - "regression on fixed rank positive semidefinite matrices a riemannian approach"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "simultaneous codeword optimization simco for dictionary update and learning"):
  with members:
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "quantization bounds on grassmann manifolds and applications to mimo communications"

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)