human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "iterative ell_1 minimization for non convex compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse optimization problem with s difference regularization")
  Member "transformed schatten 1 iterative thresholding algorithms for low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformed ell_1 regularization for learning sparse deep neural networks")
  Member "minimization of transformed l_1 penalty closed form representation and iterative thresholding algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning sparse neural networks via ell_0 and t ell_1 by a relaxed variable splitting method with application to multi scale curve classification")
  Member "l_ 1 2 regularization convergence of iterative half thresholding algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix completion via nonconvex regularization convergence of the proximal gradient algorithm")
  Member "super resolution from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "sparse approximation via penalty decomposition methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "120001 0 regularized structured sparsity convolutional neural networks")
  Member "coherence pattern guided compressive sensing with unresolved grids":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "conditioning of restricted fourier matrices and super resolution of music")

Question: Please tell me which class the source paper 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)