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 "a comprehensive survey for low rank regularization"):
  Abstract: Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many fields including machine learning, data mining and computer version. Over the last decade, much progress has been made in theories and practical applications. Nevertheless, the intersection between them is very slight. In order to construct a bridge between practical applications and theoretical research, in this paper we provide a comprehensive survey for low rank regularization. We first review several traditional machine learning models using low rank regularization, and then show their (or their variants) applications in solving practical issues, such as non-rigid structure from motion and image denoising. Subsequently, we summarize the regularizers and optimization methods that achieve great success in traditional machine learning tasks but are rarely seen in solving practical issues. Finally, we provide a discussion and comparison for some representative regularizers including convex and non-convex relaxations. Extensive experimental results demonstrate that non-convex regularizers can provide a large advantage over the nuclear norm, the regularizer widely used in solving practical issues.

Includes members:
  Member "bilinear factor matrix norm minimization for robust pca algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "side information for face completion a robust pca approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint 3d face reconstruction and dense alignment with position map regression network")
  Member "fast and accurate tensor completion with tensor trains a system identification approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep compression of sum product networks on tensor networks")
  Member "discriminative block diagonal representation learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "feature concatenation multi view subspace clustering")
  Member "missing slice recovery for tensors using a low rank model in embedded space":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "block hankel tensor arima for multiple short time series forecasting")
  Member "scalable dense non rigid structure from motion a grassmannian perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structure from articulated motion accurate and stable monocular 3d reconstruction without training data")
  Member "multi channel weighted nuclear norm minimization for real color image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "large scale low rank matrix learning with nonconvex regularizers":
    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.CV(Computer Vision and Pattern Recognition)