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 "fast low rank shared dictionary learning for image classification"):
  Abstract: Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e., claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Furthermore, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image data sets establish the advantages of our method over the state-of-the-art dictionary learning methods.

Includes members:
  Member "learning a low rank shared dictionary for object classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "signal classification under structured sparsity constraints")
  Member "adaptive matching pursuit for sparse signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "signal classification under structured sparsity constraints")
  Member "dfdl discriminative feature oriented dictionary learning for histopathological image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "signal classification under structured sparsity constraints")
  Member "task driven dictionary learning for hyperspectral image classification with structured sparsity constraints":
    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 "multi task image classification via collaborative hierarchical spike and slab priors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning a low rank shared dictionary for object classification")
  Member "collaborative multi sensor classification via sparsity based representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi modal image processing based on coupled dictionary learning")
  Member "structured priors for sparse representation based hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locality and structure regularized low rank representation for hyperspectral image classification")

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)