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: "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
  Abstract: We study the minimization problem of a non-convex sparsity promoting penalty function, the transformed $l_1$ (TL1), and its application in compressed sensing (CS). The TL1 penalty interpolates $l_0$ and $l_1$ norms through a nonnegative parameter $a \in (0,+\infty)$, similar to $l_p$ with $p \in (0,1]$, and is known to satisfy unbiasedness, sparsity and Lipschitz continuity properties. We first consider the constrained minimization problem and discuss the exact recovery of $l_0$ norm minimal solution based on the null space property (NSP). We then prove the stable recovery of $l_0$ norm minimal solution if the sensing matrix $A$ satisfies a restricted isometry property (RIP). Next, we present difference of convex algorithms for TL1 (DCATL1) in computing TL1-regularized constrained and unconstrained problems in CS. The inner loop concerns an $l_1$ minimization problem on which we employ the Alternating Direction Method of Multipliers (ADMM). For the unconstrained problem, we prove convergence of DCATL1 to a stationary point satisfying the first order optimality condition. In numerical experiments, we identify the optimal value $a=1$, and compare DCATL1 with other CS algorithms on two classes of sensing matrices: Gaussian random matrices and over-sampled discrete cosine transform matrices (DCT). We find that for both classes of sensing matrices, the performance of DCATL1 algorithm (initiated with $l_1$ minimization) always ranks near the top (if not the top), and is the most robust choice insensitive to the conditioning of the sensing matrix $A$. DCATL1 is also competitive in comparison with DCA on other non-convex penalty functions commonly used in statistics with two hyperparameters.

Connected through hyperedge HE-1 (induced by source paper "sparse optimization problem with s difference regularization"):
  with members:
    - "iterative ell_1 minimization for non convex compressed sensing"
    - "compressed sensing recovery via nonconvex shrinkage penalties"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "image reconstruction from few views by l0 norm optimization"
    - "decreasing weighted sorted ell_1 regularization"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "a study on graph structured recurrent neural networks and sparsification with application to epidemic forecasting"):
  with members:
    - "graph based deep modeling and real time forecasting of sparse spatio temporal data"
    - "laplacian smoothing gradient descent"
    - "deep learning for real time crime forecasting and its ternarization"
    - "structural rnn deep learning on spatio temporal graphs"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "iterative ell_1 minimization for non convex compressed sensing"):
  with members:
    - "towards a mathematical theory of super resolution"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "sparse signal reconstruction via iterative support detection"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "transformed schatten 1 iterative thresholding algorithms for low rank matrix completion"):
  with members:
    - "minimization of transformed l_1 penalty closed form representation and iterative thresholding algorithms"
    - "low rank matrix recovery from errors and erasures"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (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 low rank matrix completion"
    - "transformed schatten 1 iterative thresholding algorithms for matrix rank minimization and applications"
    - "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)