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 "discriminative local sparse representation by robust adaptive dictionary pair learning"):
  Abstract: In this paper, we propose a structured Robust Adaptive Dic-tionary Pair Learning (RA-DPL) framework for the discrim-inative sparse representation learning. To achieve powerful representation ability of the available samples, the setting of RA-DPL seamlessly integrates the robust projective dictionary pair learning, locality-adaptive sparse representations and discriminative coding coefficients learning into a unified learning framework. Specifically, RA-DPL improves existing projective dictionary pair learning in four perspectives. First, it applies a sparse l2,1-norm based metric to encode the recon-struction error to deliver the robust projective dictionary pairs, and the l2,1-norm has the potential to minimize the error. Sec-ond, it imposes the robust l2,1-norm clearly on the analysis dictionary to ensure the sparse property of the coding coeffi-cients rather than using the costly l0/l1-norm. As such, the robustness of the data representation and the efficiency of the learning process are jointly considered to guarantee the effi-cacy of our RA-DPL. Third, RA-DPL conceives a structured reconstruction weight learning paradigm to preserve the local structures of the coding coefficients within each class clearly in an adaptive manner, which encourages to produce the locality preserving representations. Fourth, it also considers improving the discriminating ability of coding coefficients and dictionary by incorporating a discriminating function, which can ensure high intra-class compactness and inter-class separation in the code space. Extensive experiments show that our RA-DPL can obtain superior performance over other state-of-the-arts.

Includes members:
  Member "robust subspace clustering with compressed data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convolutional dictionary pair learning network for image representation learning")
  Member "joint label prediction based semi supervised adaptive concept factorization for robust data representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space")
  Member "learning structured twin incoherent twin projective latent dictionary pairs for classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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-5 (induced by source paper "matrix cofactorization for joint representation learning and supervised classification application to hyperspectral image analysis")
  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 "convolutional dictionary pair learning network for image representation learning")
  Member "kernel induced label propagation by mapping for semi supervised classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning image specific attributes by hyperbolic neighborhood graph propagation")
  Member "cnn based joint clustering and representation learning with feature drift compensation for large scale image data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")

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)