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 "convolutional dictionary pair learning network for image representation learning"):
  Abstract: Both the Dictionary Learning (DL) and Convolutional Neural Networks (CNN) are powerful image representation learning systems based on different mechanisms and principles, however whether we can seamlessly integrate them to improve the per-formance is noteworthy exploring. To address this issue, we propose a novel generalized end-to-end representation learning architecture, dubbed Convolutional Dictionary Pair Learning Network (CDPL-Net) in this paper, which integrates the learning schemes of the CNN and dictionary pair learning into a unified framework. Generally, the architecture of CDPL-Net includes two convolutional/pooling layers and two dictionary pair learn-ing (DPL) layers in the representation learning module. Besides, it uses two fully-connected layers as the multi-layer perception layer in the nonlinear classification module. In particular, the DPL layer can jointly formulate the discriminative synthesis and analysis representations driven by minimizing the batch based reconstruction error over the flatted feature maps from the convolution/pooling layer. Moreover, DPL layer uses l1-norm on the analysis dictionary so that sparse representation can be delivered, and the embedding process will also be robust to noise. To speed up the training process of DPL layer, the efficient stochastic gradient descent is used. Extensive simulations on real databases show that our CDPL-Net can deliver enhanced performance over other state-of-the-art methods.

Includes members:
  Member "majorization minimization technique for optimally solving deep dictionary learning":
    also connected through other hyperedges:
      (none)
  Member "multilayer collaborative low rank coding network for robust deep subspace discovery":
    also connected through other hyperedges:
      (none)
  Member "robust subspace clustering with compressed data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi view subspace clustering via partition fusion")
  Member "learning structured twin incoherent twin projective latent dictionary pairs for classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "soft video multicasting using adaptive compressed sensing")
  Member "scalable block diagonal locality constrained projective dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "soft video multicasting using adaptive compressed sensing")
  Member "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix cofactorization for joint representation learning and supervised classification application to hyperspectral image analysis")
  Member "discriminative local sparse representation by robust adaptive dictionary pair learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space")
  Member "joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space")

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)