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 "locality constraint dictionary learning with support vector for pattern classification"):
  Abstract: Discriminative dictionary learning (DDL) has recently gained significant attention due to its impressive performance in various pattern classification tasks. However, the locality of atoms is not fully explored in conventional DDL approaches which hampers their classification performance. In this paper, we propose a locality constraint dictionary learning with support vector discriminative term (LCDL-SV), in which the locality information is preserved by employing the graph Laplacian matrix of the learned dictionary. To jointly learn a classifier during the training phase, a support vector discriminative term is incorporated into the proposed objective function. Moreover, in the classification stage, the identity of test data is jointly determined by the regularized residual and the learned multi-class support vector machine. Finally, the resulting optimization problem is solved by utilizing the alternative strategy. Experimental results on benchmark databases demonstrate the superiority of our proposed method over previous dictionary learning approaches on both hand-crafted and deep features. The source code of our proposed LCDL-SV is accessible at this https URL

Includes members:
  Member "scalable block diagonal locality constrained projective dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "matrix cofactorization for joint representation learning and supervised classification application to hyperspectral image analysis")
  Member "label embedded dictionary learning for image classification":
    also connected through other hyperedges:
      (none)
  Member "analysis dictionary learning based classification structure for robustness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep transform and metric learning network wedding deep dictionary learning and neural networks")
  Member "multi focus image fusion using dictionary learning and low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion")
  Member "cross label suppression a discriminative and fast dictionary learning with group regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "face recognition via locality constrained low rank representation and dictionary learning")
  Member "fast low rank shared dictionary learning for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional dictionary pair learning network for image representation learning")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)