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: "transformed schatten 1 iterative thresholding algorithms for low rank matrix completion"
  Abstract: We study a non-convex low-rank promoting penalty function, the transformed Schatten-1 (TS1), and its applications in matrix completion. The TS1 penalty, as a matrix quasi-norm defined on its singular values, interpolates the rank and the nuclear norm through a nonnegative parameter a. We consider the unconstrained TS1 regularized low-rank matrix recovery problem and develop a fixed point representation for its global minimizer. The TS1 thresholding functions are in closed analytical form for all parameter values. The TS1 threshold values differ in subcritical (supercritical) parameter regime where the TS1 threshold functions are continuous (discontinuous). We propose TS1 iterative thresholding algorithms and compare them with some state-of-the-art algorithms on matrix completion test problems. For problems with known rank, a fully adaptive TS1 iterative thresholding algorithm consistently performs the best under different conditions with ground truth matrix being multivariate Gaussian at varying covariance. For problems with unknown rank, TS1 algorithms with an additional rank estimation procedure approach the level of IRucL-q which is an iterative reweighted algorithm, non-convex in nature and best in performance.

Connected through hyperedge HE-1 (induced by source paper "transformed ell_1 regularization for learning sparse deep neural networks"):
  with members:
    - "exploring sparsity in recurrent neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "learning the number of neurons in deep networks"
    - "learning both weights and connections for efficient neural networks"
    - "deep learning in neural networks"
    - "fast convnets using group wise brain damage"
    - "structured pruning of deep convolutional neural networks"
    - "an exploration of parameter redundancy in deep networks with circulant projections"

Connected through hyperedge HE-2 (induced by source paper "dac data free automatic acceleration of convolutional networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "rehar robust and efficient human activity recognition"
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "network decoupling from regular to depthwise separable convolutions"
    - "deep neural network compression with single and multiple level quantization"

Connected through hyperedge HE-3 (induced by source paper "enhanced low rank matrix approximation"):
  with members:
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "sparsity based algorithm for detecting faults in rotating machines"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "convex denoising using non convex tight frame regularization"
    - "a note on patch based low rank minimization for fast image denoising"
    - "transformed schatten 1 iterative thresholding algorithms for matrix rank minimization and applications"
    - "patch based low rank minimization for image denoising"
    - "group sparse signal denoising non convex regularization convex optimization"

Connected through hyperedge HE-4 (induced by source paper "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"):
  with members:
    - "iterative ell_1 minimization for non convex compressed sensing"
    - "minimization of transformed l_1 penalty closed form representation and iterative thresholding algorithms"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "super resolution from noisy data"
    - "sparse approximation via penalty decomposition methods"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-5 (induced by source paper "minimization of transformed l_1 penalty closed form representation and iterative thresholding algorithms"):
  with members:
    - "transformed schatten 1 iterative thresholding algorithms for matrix rank minimization and applications"
    - "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "super resolution from noisy data"
    - "coherence pattern guided compressive sensing with unresolved grids"

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.IT(Information Theory)