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 selection via alternating linearization methods"
  Abstract: Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse covariance matrix of the Gaussian distribution, one can learn the structure of the graph by estimating a sparse inverse covariance matrix from sample data, by solving a convex maximum likelihood problem with an $\ell_1$-regularization term. In this paper, we propose a first-order method based on an alternating linearization technique that exploits the problem's special structure; in particular, the subproblems solved in each iteration have closed-form solutions. Moreover, our algorithm obtains an $\epsilon$-optimal solution in $O(1/\epsilon)$ iterations. Numerical experiments on both synthetic and real data from gene association networks show that a practical version of this algorithm outperforms other competitive algorithms.

Connected through hyperedge HE-1 (induced by source paper "gaussian graphical model exploration and selection in high dimension low sample size setting"):
  with members:
    - "projected subgradient methods for learning sparse gaussians"

Connected through hyperedge HE-2 (induced by source paper "markov random fields for collaborative filtering"):
  with members:
    - "embarrassingly shallow autoencoders for sparse data"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"
    - "density estimation using real nvp"
    - "a neural autoregressive approach to collaborative filtering"
    - "session based recommendations with recurrent neural networks"
    - "made masked autoencoder for distribution estimation"

Connected through hyperedge HE-3 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "efficient outlier removal for large scale global structure from motion"
    - "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"
    - "deterministic approximate methods for maximum consensus robust fitting"
    - "unified scalable equivalent formulations for schatten quasi norms"
    - "enhanced low rank matrix approximation"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-4 (induced by source paper "stochastic primal dual proximal extragradient descent for compositely regularized optimization"):
  with members:
    - "stochastic variance reduced admm"
    - "fast stochastic alternating direction method of multipliers"
    - "online alternating direction method"

Connected through hyperedge HE-5 (induced by source paper "on the iteration complexity analysis of stochastic primal dual hybrid gradient approach with high probability"):
  with members:
    - "fast stochastic alternating direction method of multipliers"
    - "online alternating direction method"

Connected through hyperedge HE-6 (induced by source paper "network inference via the time varying graphical lasso"):
  with members:
    - "snapvx a network based convex optimization solver"
    - "network lasso clustering and optimization in large graphs"
    - "fused multiple graphical lasso"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-7 (induced by source paper "a coordinate wise optimization algorithm for sparse inverse covariance selection"):
  with members:
    - "convex optimization for linear query processing under approximate differential privacy"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "sparse approximation via penalty decomposition methods"
    - "projected subgradient methods for learning sparse gaussians"

Connected through hyperedge HE-8 (induced by source paper "a distributed asynchronous and incremental algorithm for nonconvex optimization an admm based approach"):
  with members:
    - "a proximal dual consensus admm method for multi agent constrained optimization"
    - "parallel algorithms for constrained tensor factorization via alternating direction method of multipliers"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "optimal linear precoding strategies for wideband noncooperative systems based on game theory part i nash equilibria"

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)