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: "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"
  Abstract: Optimization methods are at the core of many problems in signal/image processing, computer vision, and machine learning. For a long time, it has been recognized that looking at the dual of an optimization problem may drastically simplify its solution. Deriving efficient strategies which jointly brings into play the primal and the dual problems is however a more recent idea which has generated many important new contributions in the last years. These novel developments are grounded on recent advances in convex analysis, discrete optimization, parallel processing, and non-smooth optimization with emphasis on sparsity issues. In this paper, we aim at presenting the principles of primal-dual approaches, while giving an overview of numerical methods which have been proposed in different contexts. We show the benefits which can be drawn from primal-dual algorithms both for solving large-scale convex optimization problems and discrete ones, and we provide various application examples to illustrate their usefulness.

Connected through hyperedge HE-1 (induced by source paper "deep transform and metric learning network wedding deep dictionary learning and neural networks"):
  with members:
    - "deep dictionary learning a parametric network approach"
    - "analysis dictionary learning an efficient and discriminative solution"
    - "analysis dictionary learning based classification structure for robustness"
    - "structured analysis dictionary learning for image classification"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-2 (induced by source paper "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan"):
  with members:
    - "dual discriminator generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "the numerics of gans"
    - "multi generator generative adversarial nets"
    - "stabilizing training of generative adversarial networks through regularization"
    - "mmd gan towards deeper understanding of moment matching network"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-3 (induced by source paper "application of preconditioned alternating direction method of multipliers in depth from focal stack"):
  with members:
    - "parallel algorithms for constrained tensor factorization via alternating direction method of multipliers"
    - "variational depth from focus reconstruction"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"

Connected through hyperedge HE-4 (induced by source paper "proximity operators of discrete information divergences"):
  with members:
    - "f divergence inequalities"
    - "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"
    - "jeffreys centroids a closed form expression for positive histograms and a guaranteed tight approximation for frequency histograms"

Connected through hyperedge HE-5 (induced by source paper "stationary signal processing on graphs"):
  with members:
    - "stationary graph processes and spectral estimation"
    - "fast robust pca on graphs"
    - "towards stationary time vertex signal processing"
    - "stationary time vertex signal processing"
    - "subsampling for graph power spectrum estimation"
    - "a probabilistic interpretation of sampling theory of graph signals"
    - "gspbox a toolbox for signal processing on graphs"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-6 (induced by source paper "sparse hierarchical interaction learning with epigraphical projection"):
  with members:
    - "nonconvex regularizations for feature selection in ranking with sparse svm"
    - "invariant scattering convolution networks"
    - "structured sparsity through convex optimization"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-7 (induced by source paper "a fast algorithm based on a sylvester like equation for ls regression with gmrf prior"):
  with members:
    - "fast spectral unmixing based on dykstra s alternating projection"
    - "fast fusion of multi band images based on solving a sylvester equation"

Connected through hyperedge HE-8 (induced by source paper "motion compensated dynamic mri reconstruction with local affine optical flow estimation"):
  with members:
    - "real time dynamic mri reconstruction using stacked denoising autoencoder"

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.NA(Numerical Analysis)