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 "group based sparse representation for image compressive sensing reconstruction with non convex regularization"):
  Abstract: Patch-based sparse representation modeling has shown great potential in image compressive sensing (CS) reconstruction. However, this model usually suffers from some limits, such as dictionary learning with great computational complexity, neglecting the relationship among similar patches. In this paper, a group-based sparse representation method with non-convex regularization (GSR-NCR) for image CS reconstruction is proposed. In GSR-NCR, the local sparsity and nonlocal self-similarity of images is simultaneously considered in a unified framework. Different from the previous methods based on sparsity-promoting convex regularization, we extend the non-convex weighted Lp (0 < p < 1) penalty function on group sparse coefficients of the data matrix, rather than conventional L1-based regularization. To reduce the computational complexity, instead of learning the dictionary with a high computational complexity from natural images, we learn the principle component analysis (PCA) based dictionary for each group. Moreover, to make the proposed scheme tractable and robust, we have developed an efficient iterative shrinkage/thresholding algorithm to solve the non-convex optimization problem. Experimental results demonstrate that the proposed method outperforms many state-of-the-art techniques for image CS reconstruction.

Includes members:
  Member "analyzing the group sparsity based on the rank minimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "image denoising via group sparsity residual constraint":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from rank estimation to rank approximation rank residual constraint for image denoising")
  Member "generalized alternating projection based total variation minimization for compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rank minimization for snapshot compressive imaging")
  Member "frist flipping and rotation invariant sparsifying transform learning and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the power of complementary regularizers image recovery via transform learning and low rank modeling")
  Member "compressive hyperspectral imaging with side information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rank minimization for snapshot compressive imaging")
  Member "group based sparse representation for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "structural group sparse representation for image compressive sensing recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")

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)