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 "low rank tensor modeling for hyperspectral unmixing accounting for spectral variability"):
  Abstract: Traditional hyperspectral unmixing methods neglect the underlying variability of spectral signatures often observed in typical hyperspectral images (HI), propagating these missmodeling errors throughout the whole unmixing process. Attempts to model material spectra as members of sets or as random variables tend to lead to severely ill-posed unmixing problems. Although parametric models have been proposed to overcome this drawback by handling endmember variability through generalizations of the mixing model, the success of these techniques depend on employing appropriate regularization strategies. Moreover, the existing approaches fail to adequately explore the natural multidimensinal representation of HIs. Recently, tensor-based strategies considered low-rank decompositions of hyperspectral images as an alternative to impose low-dimensional structures on the solutions of standard and multitemporal unmixing problems. These strategies, however, present two main drawbacks: 1) they confine the solutions to low-rank tensors, which often cannot represent the complexity of real-world scenarios; and 2) they lack guarantees that endmembers and abundances will be correctly factorized in their respective tensors. In this work, we propose a more flexible approach, called ULTRA-V, that imposes low-rank structures through regularizations whose strictness is controlled by scalar parameters. Simulations attest the superior accuracy of the method when compared with state-of-the-art unmixing algorithms that account for spectral variability.

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 "improved hyperspectral unmixing with endmember variability parametrized using an interpolated scaling tensor":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing")
  Member "a low rank tensor regularization strategy for hyperspectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (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-7 (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-8 (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)