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 "efficient dictionary learning with sparseness enforcing projections"):
  Abstract: Learning dictionaries suitable for sparse coding instead of using engineered bases has proven effective in a variety of image processing tasks. This paper studies the optimization of dictionaries on image data where the representation is enforced to be explicitly sparse with respect to a smooth, normalized sparseness measure. This involves the computation of Euclidean projections onto level sets of the sparseness measure. While previous algorithms for this optimization problem had at least quasi-linear time complexity, here the first algorithm with linear time complexity and constant space complexity is proposed. The key for this is the mathematically rigorous derivation of a characterization of the projection's result based on a soft-shrinkage function. This theory is applied in an original algorithm called Easy Dictionary Learning (EZDL), which learns dictionaries with a simple and fast-to-compute Hebbian-like learning rule. The new algorithm is efficient, expressive and particularly simple to implement. It is demonstrated that despite its simplicity, the proposed learning algorithm is able to generate a rich variety of dictionaries, in particular a topographic organization of atoms or separable atoms. Further, the dictionaries are as expressive as those of benchmark learning algorithms in terms of the reproduction quality on entire images, and result in an equivalent denoising performance. EZDL learns approximately 30 % faster than the already very efficient Online Dictionary Learning algorithm, and is therefore eligible for rapid data set analysis and problems with vast quantities of learning samples.

Includes members:
  Member "sparse activity and sparse connectivity in supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shakeout a new approach to regularized deep neural network training")
  Member "block coordinate descent for sparse nmf":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grouped sparse projection")
  Member "separable dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "feature grouping from spatially constrained multiplicative interaction":
    also connected through other hyperedges:
      (none)
  Member "estimating unknown sparsity in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "learning sparse representations of depth":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse modeling for image and vision processing")
  Member "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving robustness of deep learning based image reconstruction")
  Member "comparing measures of sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "concise explanations of neural networks using adversarial training")

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.LG(Machine Learning)