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 low rank tensor regularization strategy for hyperspectral unmixing"):
  Abstract: Tensor-based methods have recently emerged as a more natural and effective formulation to address many problems in hyperspectral imaging. In hyperspectral unmixing (HU), low-rank constraints on the abundance maps have been shown to act as a regularization which adequately accounts for the multidimensional structure of the underlying signal. However, imposing a strict low-rank constraint for the abundance maps does not seem to be adequate, as important information that may be required to represent fine scale abundance behavior may be discarded. This paper introduces a new low-rank tensor regularization that adequately captures the low-rank structure underlying the abundance maps without hindering the flexibility of the solution. Simulation results with synthetic and real data show that the the extra flexibility introduced by the proposed regularization significantly improves the unmixing results.

Includes members:
  Member "a data dependent multiscale model for hyperspectral unmixing with spectral variability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing")
  Member "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing")
  Member "generalized linear mixing model accounting for endmember variability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing")
  Member "tech report a fast multiscale spatial regularization for sparse hyperspectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "super resolution reconstruction of electrical impedance tomography images")
  Member "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing")
  Member "tensor decompositions for signal processing applications from two way to multiway component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tensor decompositions in deep learning")

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)