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 "multilayer convolutional sparse modeling pursuit and dictionary learning"):
  Abstract: The recently proposed multilayer convolutional sparse coding (ML-CSC) model, consisting of a cascade of convolutional sparse layers, provides a new interpretation of convolutional neural networks (CNNs). Under this framework, the forward pass in a CNN is equivalent to a pursuit algorithm aiming to estimate the nested sparse representation vectors from a given input signal. Despite having served as a pivotal connection between CNNs and sparse modeling, a deeper understanding of the ML-CSC is still lacking. In this paper, we propose a sound pursuit algorithm for the ML-CSC model by adopting a projection approach. We provide new and improved bounds on the stability of the solution of such pursuit and we analyze different practical alternatives to implement this in practice. We show that the training of the filters is essential to allow for nontrivial signals in the model, and we derive an online algorithm to learn the dictionaries from real data, effectively resulting in cascaded sparse convolutional layers. Last, but not least, we demonstrate the applicability of the ML-CSC model for several applications in an unsupervised setting, providing competitive results. Our work represents a bridge between matrix factorization, sparse dictionary learning, and sparse autoencoders, and we analyze these connections in detail.

Includes members:
  Member "convolutional dictionary learning via local processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "towards understanding residual and dilated dense neural networks via convolutional sparse coding")
  Member "online convolutional dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a local block coordinate descent algorithm for the convolutional sparse coding model")
  Member "trainlets dictionary learning in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dictionary learning for adaptive gpr landmine classification")
  Member "maximal sparsity with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "flexible multilayer sparse approximations of matrices and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning fast algorithms for linear transforms using butterfly factorizations")
  Member "on the expressive power of deep learning a tensor analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tensor networks for language modeling")
  Member "winner take all autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "memory augmented generative adversarial networks for anomaly detection")

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.CV(Computer Vision and Pattern Recognition)