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 "large scale low rank matrix learning with nonconvex regularizers"):
  Abstract: Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better empirical performance. However, the resulting optimization problem is much more challenging. Recent state-of-the-art requires an expensive full SVD in each iteration. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, a cutoff can be derived to automatically threshold the singular values obtained from the proximal operator. This allows such operator being efficiently approximated by power method. Based on it, we develop a proximal gradient algorithm (and its accelerated variant) with inexact proximal splitting and prove that a convergence rate of O(1/T) where T is the number of iterations is guaranteed. Furthermore, we show the proposed algorithm can be well parallelized, which achieves nearly linear speedup w.r.t the number of threads. Extensive experiments are performed on matrix completion and robust principal component analysis, which shows a significant speedup over the state-of-the-art. Moreover, the matrix solution obtained is more accurate and has a lower rank than that of the nuclear norm regularizer.

Includes members:
  Member "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "partial sum minimization of singular values in robust pca algorithm and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "fast randomized singular value thresholding for nuclear norm minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bilinear factor matrix norm minimization for robust pca algorithms and applications")
  Member "fast low rank matrix learning with nonconvex regularization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive structure constrained robust latent low rank coding for image recovery")
  Member "orthogonal rank one matrix pursuit for low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "binary matrix completion with nonconvex regularizers")
  Member "generalized singular value thresholding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "robust recovery of subspace structures by low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "binary matrix completion with nonconvex regularizers")

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.LG(Machine Learning)