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 "on learning with shift invariant structures"):
  Abstract: We describe new results and algorithms for two different, but related, problems which deal with circulant matrices: learning shift-invariant components from training data and calculating the shift (or alignment) between two given signals. In the first instance, we deal with the shift-invariant dictionary learning problem while the latter bears the name of (compressive) shift retrieval. We formulate these problems using circulant and convolutional matrices (including unions of such matrices), define optimization problems that describe our goals and propose efficient ways to solve them. Based on these findings, we also show how to learn a wavelet-like dictionary from training data. We connect our work with various previous results from the literature and we show the effectiveness of our proposed algorithms using synthetic, ECG signals and images.

Includes members:
  Member "convolutional dictionary learning a comparative review and new algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "dictionary learning for adaptive gpr target classification":
    also connected through other hyperedges:
      (none)
  Member "working locally thinking globally theoretical guarantees for convolutional sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards understanding residual and dilated dense neural networks via convolutional sparse coding")
  Member "optimization methods for convolutional sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional dictionary learning acceleration and convergence")
  Member "compressive shift retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressive signal processing with circulant sensing matrices")
  Member "shift invariance sparse coding for audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep dictionary learning a parametric network approach")
  Member "circulant and toeplitz matrices in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on dimension free tail inequalities for sums of random matrices and applications")

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)