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 data dependent multiscale model for hyperspectral unmixing with spectral variability"):
  Abstract: Spectral variability in hyperspectral images can result from factors including environmental, illumination, atmospheric and temporal changes. Its occurrence may lead to the propagation of significant estimation errors in the unmixing process. To address this issue, extended linear mixing models have been proposed which lead to large scale nonsmooth ill-posed inverse problems. Furthermore, the regularization strategies used to obtain meaningful results have introduced interdependencies among abundance solutions that further increase the complexity of the resulting optimization problem. In this paper we present a novel data dependent multiscale model for hyperspectral unmixing accounting for spectral variability. The new method incorporates spatial contextual information to the abundances in the Extended Linear Mixing Model by using a multiscale transform based on superpixels. The proposed method results in a fast algorithm that solves the abundance problem only once in each scale during each iteration. Simulation results using synthetic and real images compare the performances, both in accuracy and execution time, of the proposed algorithm and other state-of-the-art solutions.

Includes members:
  Member "deep generative endmember modeling an application to unsupervised spectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep generative models for library augmentation in multiple endmember spectral mixture analysis")
  Member "an augmented linear mixing model to address spectral variability for hyperspectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks")
  Member "a low rank tensor regularization strategy for hyperspectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-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)