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: "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.

Connected through hyperedge HE-1 (induced by source paper "multilayer convolutional sparse modeling pursuit and dictionary learning"):
  with members:
    - "convolutional dictionary learning via local processing"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "trainlets dictionary learning in high dimensions"
    - "maximal sparsity with deep networks"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "on the expressive power of deep learning a tensor analysis"
    - "winner take all autoencoders"
    - "k sparse autoencoders"

Connected through hyperedge HE-2 (induced by source paper "a local block coordinate descent algorithm for the convolutional sparse coding model"):
  with members:
    - "multi focus image fusion via coupled sparse representation and dictionary learning"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "scalable online convolutional sparse coding"
    - "trainlets dictionary learning in high dimensions"
    - "an overview of gradient descent optimization algorithms"
    - "sparse modeling for image and vision processing"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-3 (induced by source paper "convolutional dictionary learning a comparative review and new algorithms"):
  with members:
    - "convolutional dictionary learning acceleration and convergence"
    - "first and second order methods for online convolutional dictionary learning"
    - "synthesis versus analysis in patch based image priors"
    - "online convolutional dictionary learning for multimodal imaging"
    - "convolutional sparse coding for high dynamic range imaging"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-4 (induced by source paper "online convolutional sparse coding with sample dependent dictionary"):
  with members:
    - "convolutional dictionary learning via local processing"
    - "online convolutional dictionary learning for multimodal imaging"
    - "scalable online convolutional sparse coding"
    - "learning feed forward one shot learners"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-5 (induced by source paper "scalable online convolutional sparse coding"):
  with members:
    - "online convolutional dictionary learning for multimodal imaging"
    - "convolutional sparse coding for high dynamic range imaging"

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.LG(Machine Learning)