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 "improved hyperspectral unmixing with endmember variability parametrized using an interpolated scaling tensor"):
  Abstract: Endmember (EM) variability has an important impact on the performance of hyperspectral image (HI) analysis algorithms. Recently, extended linear mixing models have been proposed to account for EM variability in the spectral unmixing (SU) problem. The direct use of these models has led to severely ill-posed optimization problems. Different regularization strategies have been considered to deal with this issue, but none so far has consistently exploited the information provided by the existence of multiple pure pixels often present in HIs. In this work, we propose to break the SU problem into a sequence of two problems. First, we use pure pixel information to estimate an interpolated tensor of scaling factors representing spectral variability. This is done by considering the spectral variability to be a smooth function over the HI and confining the energy of the scaling tensor to a low-rank structure. Afterwards, we solve a matrix-factorization problem to estimate the fractional abundances using the variability scaling factors estimated in the previous step, what leads to a significantly more well-posed problem. Simulation swith synthetic and real data attest the effectiveness of the proposed strategy.

Includes members:
  Member "a low rank tensor regularization strategy for hyperspectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing")
  Member "a data dependent multiscale model for hyperspectral unmixing with spectral variability":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing")
  Member "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion")
  Member "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing")
  Member "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing")

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)