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: "sparse inverse covariance matrix estimation using quadratic approximation"
  Abstract: The L1-regularized Gaussian maximum likelihood estimator (MLE) has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix, or alternatively the underlying graph structure of a Gaussian Markov Random Field, from very limited samples. We propose a novel algorithm for solving the resulting optimization problem which is a regularized log-determinant program. In contrast to recent state-of-the-art methods that largely use first order gradient information, our algorithm is based on Newton's method and employs a quadratic approximation, but with some modifications that leverage the structure of the sparse Gaussian MLE problem. We show that our method is superlinearly convergent, and present experimental results using synthetic and real-world application data that demonstrate the considerable improvements in performance of our method when compared to other state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "learning graphs from data a signal representation perspective"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "graph learning under sparsity priors"
    - "blind identification of graph filters"
    - "network inference via the time varying graphical lasso"
    - "graph based transform coding with application to image compression"
    - "signal processing on graphs causal modeling of un structured data"
    - "network topology inference from spectral templates"

Connected through hyperedge HE-2 (induced by source paper "federated multi task learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "distributed multi task relationship learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "asynchronous multi task learning"
    - "distributed multi task learning with shared representation"
    - "tensor switching networks"

Connected through hyperedge HE-3 (induced by source paper "an inexact subsampled proximal newton type method for large scale machine learning"):
  with members:
    - "uniform sampling for matrix approximation"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "fast approximation of matrix coherence and statistical leverage"

Connected through hyperedge HE-4 (induced by source paper "a constrained l1 minimization approach for estimating multiple sparse gaussian or nonparanormal graphical models"):
  with members:
    - "learning structural changes of gaussian graphical models in controlled experiments"
    - "density difference estimation"

Connected through hyperedge HE-5 (induced by source paper "proximal quasi newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates"):
  with members:
    - "practical inexact proximal quasi newton method with global complexity analysis"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-6 (induced by source paper "proximal quasi newton methods for convex optimization"):
  with members:
    - "practical inexact proximal quasi newton method with global complexity analysis"

Connected through hyperedge HE-7 (induced by source paper "structured sparsity discrete and convex approaches"):
  with members:
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "group sparse model selection hardness and relaxations"
    - "a novel sparsity and clustering regularization"
    - "on model based rip 1 matrices"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "phase retrieval via matrix completion"
    - "convex approaches to model wavelet sparsity patterns"

Connected through hyperedge HE-8 (induced by source paper "exact hybrid covariance thresholding for joint graphical lasso"):
  with members:
    - "fused multiple graphical lasso"

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)