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: "a unified alternating direction method of multipliers by majorization minimization"
  Abstract: Accompanied with the rising popularity of compressed sensing, the Alternating Direction Method of Multipliers (ADMM) has become the most widely used solver for linearly constrained convex problems with separable objectives. In this work, we observe that many previous variants of ADMM update the primal variable by minimizing different majorant functions with their convergence proofs given case by case. Inspired by the principle of majorization minimization, we respectively present the unified frameworks and convergence analysis for the Gauss-Seidel ADMMs and Jacobian ADMMs, which use different historical information for the current updating. Our frameworks further generalize previous ADMMs to the ones capable of solving the problems with non-separable objectives by minimizing their separable majorant surrogates. We also show that the bound which measures the convergence speed of ADMMs depends on the tightness of the used majorant function. Then several techniques are introduced to improve the efficiency of ADMMs by tightening the majorant functions. In particular, we propose the Mixed Gauss-Seidel and Jacobian ADMM (M-ADMM) which alleviates the slow convergence issue of Jacobian ADMMs by absorbing merits of the Gauss-Seidel ADMMs. M-ADMM can be further improved by using backtracking, wise variable partition and fully exploiting the structure of the constraint. Beyond the guarantee in theory, numerical experiments on both synthesized and real-world data further demonstrate the superiority of our new ADMMs in practice. Finally, we release a toolbox at this https URL that implements efficient ADMMs for many problems in compressed sensing.

Connected through hyperedge HE-1 (induced by source paper "an analysis of noise folding for low rank matrix recovery"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "noise folding in compressed sensing"
    - "robust principal component analysis"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"

Connected through hyperedge HE-2 (induced by source paper "robust subspace clustering with compressed data"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "automatic subspace learning via principal coefficients embedding"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"

Connected through hyperedge HE-3 (induced by source paper "tensor q rank a new data dependent tensor rank"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "parallel matrix factorization for low rank tensor completion"
    - "nuclear norm of higher order tensors"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "a new convex relaxation for tensor completion"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-4 (induced by source paper "exact recovery of tensor robust principal component analysis under linear transforms"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "incoherence optimal matrix completion"
    - "exact tensor completion using t svd"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "a new convex relaxation for tensor completion"
    - "the power and arnoldi methods in an algebra of circulants"
    - "most tensor problems are np hard"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "a distributed method for fitting laplacian regularized stratified models"):
  with members:
    - "network inference via the time varying graphical lasso"
    - "a survey on multi task learning"
    - "sparse matrix factorizations for fast linear solvers with application to laplacian systems"
    - "snap a general purpose network analysis and graph mining library"
    - "snapvx a network based convex optimization solver"
    - "network lasso clustering and optimization in large graphs"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-6 (induced by source paper "subspace clustering by block diagonal representation"):
  with members:
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "guess who rated this movie identifying users through subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-7 (induced by source paper "efficient multi domain dictionary learning with gans"):
  with members:
    - "structured analysis dictionary learning for image classification"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "a comprehensive analysis of deep learning based representation for face recognition"
    - "regularized robust coding for face recognition"

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)