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 "multilayer collaborative low rank coding network for robust deep subspace discovery"):
  Abstract: For subspace recovery, most existing low-rank representation (LRR) models performs in the original space in single-layer mode. As such, the deep hierarchical information cannot be learned, which may result in inaccurate recoveries for complex real data. In this paper, we explore the deep multi-subspace recovery problem by designing a multilayer architecture for latent LRR. Technically, we propose a new Multilayer Collabora-tive Low-Rank Representation Network model termed DeepLRR to discover deep features and deep subspaces. In each layer (>2), DeepLRR bilinearly reconstructs the data matrix by the collabo-rative representation with low-rank coefficients and projection matrices in the previous layer. The bilinear low-rank reconstruc-tion of previous layer is directly fed into the next layer as the input and low-rank dictionary for representation learning, and is further decomposed into a deep principal feature part, a deep salient feature part and a deep sparse error. As such, the coher-ence issue can be also resolved due to the low-rank dictionary, and the robustness against noise can also be enhanced in the feature subspace. To recover the sparse errors in layers accurately, a dynamic growing strategy is used, as the noise level will be-come smaller for the increase of layers. Besides, a neighborhood reconstruction error is also included to encode the locality of deep salient features by deep coefficients adaptively in each layer. Extensive results on public databases show that our DeepLRR outperforms other related models for subspace discovery and clustering.

Includes members:
  Member "robust graph learning from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "outlier detection ensemble with embedded feature selection")
  Member "robust subspace clustering with compressed data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional dictionary pair learning network for image representation learning")
  Member "joint label prediction based semi supervised adaptive concept factorization for robust data representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space")
  Member "robust subspace discovery by block diagonal adaptive locality constrained representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space")
  Member "robust triple matrix recovery based auto weighted label propagation for classification":
    also connected through other hyperedges:
      (none)
  Member "discriminative local sparse representation by robust adaptive dictionary pair learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional dictionary pair learning network for image representation learning")
  Member "adaptive structure constrained robust latent low rank coding for image recovery":
    also connected through other hyperedges:
      (none)
  Member "subspace clustering by block diagonal representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic k means clustering via nonlinear programming")

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)