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 deep spectral convolution network for hyperspectral unmixing with multinomial mixture kernel and endmember uncertainty"):
  Abstract: In this study, we propose a novel framework for hyperspectral unmixing by using a modular neural network structure while addressing the endmember uncertainty in our formulation. We present critical contributions throughout the manuscript: First, to improve the separability for hyperspectral data, we modify deep spectral convolution networks (DSCNs) that lead to more stable and accurate results. Second, we introduce a multinomial mixture kernel with a neural network (NN) which mimics the Gaussian Mixture Model (GMM) to estimate the abundances per-pixel by using the low-dimension representations obtained from the improved DSCN. Moreover, as formulated in the spectral variability assumption, a NN module is incorporated to capture the uncertainty term. Third, to optimize the coefficients of the multinomial model and the uncertainty term, Wasserstein GAN is exploited in particular since it has several theoretical benefits over the expectation maximization. Fourth, all neural network modules are formulated as an end-to-end hyperspectral unmixing pipeline that can be optimized with backpropagation by using a stochastic gradient-based solver. Experiments are performed on real and synthetic datasets. The results validate that the proposed method obtains state-of-the-art hyperspectral unmixing performance particularly on the real datasets compared to the baseline techniques.

Includes members:
  Member "deep spectral convolution network for hyperspectral unmixing":
    also connected through other hyperedges:
      (none)
  Member "a gaussian mixture model representation of endmember variability in hyperspectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hicore visual hierarchical context reasoning")
  Member "sliced wasserstein distance for learning gaussian mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularized wasserstein means for aligning distributional data")
  Member "endnet sparse autoencoder network for endmember extraction and hyperspectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for classification of hyperspectral data a comparative review")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "photo realistic single image super resolution using a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "a spatial compositional model for linear unmixing and endmember uncertainty estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep spectral convolution network for hyperspectral unmixing")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)