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 riemannian geometry for low rank matrix completion"
  Abstract: We propose a new Riemannian geometry for fixed-rank matrices that is specifically tailored to the low-rank matrix completion problem. Exploiting the degree of freedom of a quotient space, we tune the metric on our search space to the particular least square cost function. At one level, it illustrates in a novel way how to exploit the versatile framework of optimization on quotient manifold. At another level, our algorithm can be considered as an improved version of LMaFit, the state-of-the-art Gauss-Seidel algorithm. We develop necessary tools needed to perform both first-order and second-order optimization. In particular, we propose gradient descent schemes (steepest descent and conjugate gradient) and trust-region algorithms. We also show that, thanks to the simplicity of the cost function, it is numerically cheap to perform an exact linesearch given a search direction, which makes our algorithms competitive with the state-of-the-art on standard low-rank matrix completion instances.

Connected through hyperedge HE-1 (induced by source paper "accelerated alternating projections for robust principal component analysis"):
  with members:
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "fast algorithms for robust pca via gradient descent"
    - "understanding alternating minimization for matrix completion"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "topological interference management with user admission control via riemannian optimization"):
  with members:
    - "fundamental tradeoff between storage and latency in cache aided wireless interference networks"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "wireless communications in the era of big data"
    - "heterogeneous cellular networks using wireless backhaul fast admission control and large system analysis"

Connected through hyperedge HE-3 (induced by source paper "a sparse and low rank optimization framework for index coding via riemannian optimization"):
  with members:
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "wireless communications in the era of big data"
    - "large scale convex optimization for ultra dense cloud ran"
    - "fundamental limits of caching in wireless d2d networks"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "fundamental limits of caching"
    - "an equivalence between network coding and index coding"

Connected through hyperedge HE-4 (induced by source paper "scaled stochastic gradient descent for low rank matrix completion"):
  with members:
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "low rank matrix completion with noisy observations a quantitative comparison"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "scalable nuclear norm minimization by subspace pursuit proximal riemannian gradient"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "efficient and practical stochastic subgradient descent for nuclear norm regularization"
    - "a hybrid algorithm for convex semidefinite optimization"
    - "large scale convex minimization with a low rank constraint"
    - "low rank matrix recovery from errors and erasures"
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "fast optimization algorithm on riemannian manifolds and its application in low rank representation"):
  with members:
    - "scalable nuclear norm minimization by subspace pursuit proximal riemannian gradient"
    - "heterogeneous tensor decomposition for clustering via manifold optimization"
    - "extrinsic methods for coding and dictionary learning on grassmann manifolds"
    - "robust recovery of subspace structures by low rank representation"
    - "fixed rank matrix factorizations and riemannian low rank optimization"

Connected through hyperedge HE-7 (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"
    - "recovering low rank matrices from few coefficients in any basis"
    - "moving object detection by detecting contiguous outliers in the low rank representation"

Connected through hyperedge HE-8 (induced by source paper "fixed rank matrix factorizations and riemannian low rank optimization"):
  with members:
    - "manopt a matlab toolbox for optimization on manifolds"
    - "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"
    - "a geometric approach to low rank matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from noisy entries"
    - "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.LG(Machine Learning)