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: "distributed matrix completion and robust factorization"
  Abstract: If learning methods are to scale to the massive sizes of modern datasets, it is essential for the field of machine learning to embrace parallel and distributed computing. Inspired by the recent development of matrix factorization methods with rich theory but poor computational complexity and by the relative ease of mapping matrices onto distributed architectures, we introduce a scalable divide-and-conquer framework for noisy matrix factorization. We present a thorough theoretical analysis of this framework in which we characterize the statistical errors introduced by the "divide" step and control their magnitude in the "conquer" step, so that the overall algorithm enjoys high-probability estimation guarantees comparable to those of its base algorithm. We also present experiments in collaborative filtering and video background modeling that demonstrate the near-linear to superlinear speed-ups attainable with this approach.

Connected through hyperedge HE-1 (induced by source paper "federated over the air subspace learning from incomplete data"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "provable subspace tracking from missing data and matrix completion"
    - "federated learning over wireless fading channels"
    - "federated pca with adaptive rank estimation"
    - "federated machine learning concept and applications"
    - "machine learning in the air"
    - "byzantine stochastic gradient descent"

Connected through hyperedge HE-2 (induced by source paper "federated clustering via matrix factorization models from model averaging to gradient sharing"):
  with members:
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "agnostic federated learning"
    - "clustering by orthogonal nmf model and non convex penalty optimization"
    - "secure federated matrix factorization"
    - "federated machine learning concept and applications"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "learning from hidden traits joint factor analysis and latent clustering"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-3 (induced by source paper "robust and scalable column row sampling from corrupted big data"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "spatial random sampling a structure preserving data sketching tool"
    - "innovation pursuit a new approach to subspace clustering"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"

Connected through hyperedge HE-4 (induced by source paper "big data and the internet of things"):
  with members:
    - "how to use big data technologies to optimize operations in upstream petroleum industry"
    - "the missing piece in complex analytics low latency scalable model management and serving with velox"
    - "sensing as a service and big data"
    - "internet of things iot a vision architectural elements and future directions"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-5 (induced by source paper "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"):
  with members:
    - "innovation pursuit a new approach to subspace clustering"
    - "screen content image segmentation using sparse smooth decomposition"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "screen content image segmentation using least absolute deviation fitting"
    - "performance guarantees for reprocs correlated low rank matrix entries case"
    - "a subspace method for array covariance matrix estimation"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"

Connected through hyperedge HE-6 (induced by source paper "revisiting the nystrom method for improved large scale machine learning"):
  with members:
    - "matrix coherence and the nystrom method"
    - "improving cur matrix decomposition and the nystr o m approximation via adaptive sampling"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "sharp analysis of low rank kernel matrix approximations"
    - "stochastic low rank kernel learning for regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "localization on low order eigenvectors of data matrices"

Connected through hyperedge HE-7 (induced by source paper "language modeling with power low rank ensembles"):
  with members:
    - "a fast and simple algorithm for training neural probabilistic language models"
    - "exact matrix completion via convex optimization"
    - "classes for fast maximum entropy training"

Connected through hyperedge HE-8 (induced by source paper "randomized algorithms for matrices and data"):
  with members:
    - "matrix coherence and the nystrom method"
    - "fast approximation of matrix coherence and statistical leverage"
    - "lsrn a parallel iterative solver for strongly over or under determined systems"
    - "a sparse johnson lindenstrauss transform"
    - "row sampling for matrix algorithms via a non commutative bernstein bound"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "sparser johnson lindenstrauss transforms"
    - "computation in large scale scientific and internet data applications is a focus of mmds 2010"

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)