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 for multimodal imaging"
  Abstract: Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems. In this paper, we propose a new method that reconstructs multimodal images from their linear measurements by exploiting redundancies across different modalities. Our method combines a convolutional group-sparse representation of images with total variation (TV) regularization for high-quality multimodal imaging. We develop an online algorithm that enables the unsupervised learning of convolutional dictionaries on large-scale datasets that are typical in such applications. We illustrate the benefit of our approach in the context of joint intensity-depth imaging.

Connected through hyperedge HE-1 (induced by source paper "an online plug and play algorithm for regularized image reconstruction"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "seagle sparsity driven image reconstruction under multiple scattering"
    - "plug and play unplugged optimization free reconstruction using consensus equilibrium"

Connected through hyperedge HE-2 (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"
    - "convolutional sparse coding for high dynamic range imaging"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-3 (induced by source paper "online convolutional sparse coding with sample dependent dictionary"):
  with members:
    - "convolutional dictionary learning via local processing"
    - "scalable online convolutional sparse coding"
    - "online convolutional dictionary learning"
    - "learning feed forward one shot learners"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-4 (induced by source paper "motion guided lidar camera autocalibration and accelerated depth super resolution"):
  with members:
    - "high resolution lidar based depth mapping using bilateral filter"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-5 (induced by source paper "online convolutional dictionary learning"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "convolutional dictionary learning acceleration and convergence"
    - "convolutional dictionary learning via local processing"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "scalable online convolutional sparse coding"
    - "a survey of sparse representation algorithms and applications"
    - "a fast patch dictionary method for whole image recovery"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-6 (induced by source paper "scalable online convolutional sparse coding"):
  with members:
    - "online convolutional dictionary learning"
    - "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.CV(Computer Vision and Pattern Recognition)