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: "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
  Abstract: Many problems in machine learning and other fields can be (re)for-mulated as linearly constrained separable convex programs. In most of the cases, there are multiple blocks of variables. However, the traditional alternating direction method (ADM) and its linearized version (LADM, obtained by linearizing the quadratic penalty term) are for the two-block case and cannot be naively generalized to solve the multi-block case. So there is great demand on extending the ADM based methods for the multi-block case. In this paper, we propose LADM with parallel splitting and adaptive penalty (LADMPSAP) to solve multi-block separable convex programs efficiently. When all the component objective functions have bounded subgradients, we obtain convergence results that are stronger than those of ADM and LADM, e.g., allowing the penalty parameter to be unbounded and proving the sufficient and necessary conditions} for global convergence. We further propose a simple optimality measure and reveal the convergence rate of LADMPSAP in an ergodic sense. For programs with extra convex set constraints, with refined parameter estimation we devise a practical version of LADMPSAP for faster convergence. Finally, we generalize LADMPSAP to handle programs with more difficult objective functions by linearizing part of the objective function as well. LADMPSAP is particularly suitable for sparse representation and low-rank recovery problems because its subproblems have closed form solutions and the sparsity and low-rankness of the iterates can be preserved during the iteration. It is also highly parallelizable and hence fits for parallel or distributed computing. Numerical experiments testify to the advantages of LADMPSAP in speed and numerical accuracy.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of admm with task adaption and beyond"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "regularization by denoising clarifications and new interpretations"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "density aware single image de raining using a multi stream dense network"
    - "a theoretically guaranteed deep optimization framework for robust compressive sensing mri"
    - "toward designing convergent deep operator splitting methods for task specific nonconvex optimization"
    - "clearing the skies a deep network architecture for single image rain removal"

Connected through hyperedge HE-2 (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"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "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"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-3 (induced by source paper "research on clustering performance of sparse subspace clustering"):
  with members:
    - "subspace clustering using a symmetric low rank representation"
    - "symmetric low rank representation for subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "evolutionary self expressive models for subspace clustering"):
  with members:
    - "evolutionary clustering via message passing"
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "theoretical analysis of sparse subspace clustering with missing entries"
    - "noisy subspace clustering via matching pursuits"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"

Connected through hyperedge HE-5 (induced by source paper "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs"):
  with members:
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "efficient sparse subspace clustering by nearest neighbour filtering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "provable deterministic leverage score sampling"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-6 (induced by source paper "efficient sparse subspace clustering by nearest neighbour filtering"):
  with members:
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"
    - "generalized principal component analysis gpca"
    - "fixed rank representation for unsupervised visual learning"

Connected through hyperedge HE-7 (induced by source paper "adaptive relaxed admm convergence theory and practical implementation"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "non negative factorization of the occurrence tensor from financial contracts"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "adaptive admm with spectral penalty parameter selection"
    - "training neural networks without gradients a scalable admm approach"
    - "efficient training of very deep neural networks for supervised hashing"
    - "fast admm algorithm for distributed optimization with adaptive penalty"
    - "unwrapping admm efficient distributed computing via transpose reduction"

Connected through hyperedge HE-8 (induced by source paper "collaborative low rank subspace clustering"):
  with members:
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "fixed rank representation for unsupervised visual learning"
    - "sparse subspace clustering algorithm theory and applications"
    - "building high level features using large scale unsupervised learning"
    - "analysis and improvement of low rank representation for subspace segmentation"
    - "robust principal component analysis"

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)