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 "online convolutional dictionary learning"):
  Abstract: While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem. The single biggest impediment to learning from large training sets is the memory requirements, which grow at least linearly with the size of the training set since all existing methods are batch algorithms. The work reported here addresses this limitation by extending online dictionary learning ideas to the convolutional context.

Includes members:
  Member "multilayer convolutional sparse modeling pursuit and dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic guarantees for inverse imaging with untrained network priors")
  Member "convolutional dictionary learning acceleration and convergence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional dictionary pair learning network for image representation learning")
  Member "convolutional dictionary learning via local processing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "orthogonal convolutional neural networks")
  Member "working locally thinking globally theoretical guarantees for convolutional sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards understanding residual and dilated dense neural networks via convolutional sparse coding")
  Member "online convolutional dictionary learning for multimodal imaging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an online plug and play algorithm for regularized image reconstruction")
  Member "scalable online convolutional sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a local block coordinate descent algorithm for the convolutional sparse coding model")
  Member "a survey of sparse representation algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks")
  Member "a fast patch dictionary method for whole image recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "differentiable linearized admm")

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)