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: "understanding alternating minimization for matrix completion"
  Abstract: Alternating Minimization is a widely used and empirically successful heuristic for matrix completion and related low-rank optimization problems. Theoretical guarantees for Alternating Minimization have been hard to come by and are still poorly understood. This is in part because the heuristic is iterative and non-convex in nature. We give a new algorithm based on Alternating Minimization that provably recovers an unknown low-rank matrix from a random subsample of its entries under a standard incoherence assumption. Our results reduce the sample size requirements of the Alternating Minimization approach by at least a quartic factor in the rank and the condition number of the unknown matrix. These improvements apply even if the matrix is only close to low-rank in the Frobenius norm. Our algorithm runs in nearly linear time in the dimension of the matrix and, in a broad range of parameters, gives the strongest sample bounds among all subquadratic time algorithms that we are aware of. #R##N#Underlying our work is a new robust convergence analysis of the well-known Power Method for computing the dominant singular vectors of a matrix. This viewpoint leads to a conceptually simple understanding of Alternating Minimization. In addition, we contribute a new technique for controlling the coherence of intermediate solutions arising in iterative algorithms based on a smoothed analysis of the QR factorization. These techniques may be of interest beyond their application here.

Connected through hyperedge HE-1 (induced by source paper "recommendation on a budget column space recovery from partially observed entries with random or active sampling"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "streaming principal component analysis from incomplete data"
    - "matrix completion has no spurious local minimum"
    - "subspace learning with partial information"
    - "faster subset selection for matrices and applications"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-2 (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"
    - "sparse subspace clustering algorithm theory and applications"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion"):
  with members:
    - "phase retrieval without small ball probability assumptions"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "blind demixing and deconvolution at near optimal rate"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "low rank matrix recovery from clifford orbits"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-5 (induced by source paper "fighting fire with fire using antidote data to improve polarization and fairness of recommender systems"):
  with members:
    - "equity of attention amortizing individual fairness in rankings"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "fairness of exposure in rankings"
    - "a reductions approach to fair classification"
    - "stealing hyperparameters in machine learning"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "a convex framework for fair regression"
    - "beyond parity fairness objectives for collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "iterative collaborative filtering for sparse noisy tensor estimation"):
  with members:
    - "exact tensor completion with sum of squares"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "spectral algorithms for tensor completion"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "nuclear norm of higher order tensors"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-7 (induced by source paper "robust matrix completion via maximum correntropy criterion and half quadratic optimization"):
  with members:
    - "guaranteed matrix completion via non convex factorization"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "matrix completion from a few entries"
    - "matrix completion with noise"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "matrix completion and performance guarantees for single individual haplotyping"):
  with members:
    - "fast exact matrix completion with finite samples"
    - "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)