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: "decreasing weighted sorted ell_1 regularization"
  Abstract: We consider a new family of regularizers, termed {\it weighted sorted $\ell_1$ norms} (WSL1), which generalizes the recently introduced {\it octagonal shrinkage and clustering algorithm for regression} (OSCAR) and also contains the $\ell_1$ and $\ell_{\infty}$ norms as particular instances. We focus on a special case of the WSL1, the {\sl decreasing WSL1} (DWSL1), where the elements of the argument vector are sorted in non-increasing order and the weights are also non-increasing. In this paper, after showing that the DWSL1 is indeed a norm, we derive two key tools for its use as a regularizer: the dual norm and the Moreau proximity operator.

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"
    - "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
    - "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 "asymptotics and optimal designs of slope for sparse linear regression"):
  with members:
    - "precise error analysis of regularized m estimators in high dimensions"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "parameter estimation with the ordered 120001 2 regularization via an alternating direction method of multipliers"):
  with members:
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-4 (induced by source paper "nonconvex penalties with analytical solutions for one bit compressive sensing"):
  with members:
    - "enhanced low rank matrix approximation"
    - "pinball loss minimization for one bit compressive sensing convex models and algorithms"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-5 (induced by source paper "randomized gradient boosting machine"):
  with members:
    - "a primal dual convergence analysis of boosting"

Connected through hyperedge HE-6 (induced by source paper "the ordered weighted ell_1 norm atomic formulation projections and algorithms"):
  with members:
    - "structured sparsity through convex optimization"

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.CV(Computer Vision and Pattern Recognition)