human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a spatial compositional model for linear unmixing and endmember uncertainty estimation"
  Abstract: The normal compositional model (NCM) has been extensively used in hyperspectral unmixing. However, previous research has mostly focused on the estimation of endmembers and/or their variability, based on the assumption that the pixels are independent random variables. In this paper, we show that this assumption does not hold if all the pixels are generated by a fixed endmember set. This introduces another concept,  endmember uncertainty , which is related to whether the pixels fit into the endmember simplex. To further develop this idea, we derive the NCM from the ground up without the pixel independence assumption, along with: 1) using different noise levels at different wavelengths and 2) using a spatial and sparsity promoting prior for the abundances. The resulting new formulation is called the  spatial compositional model  (SCM) to better differentiate it from the NCM. The SCM maximum  a posteriori  objective leads to an optimization problem featuring Noise-weighted least-squares minimization for unmixing. The problem is solved by projected gradient descent, resulting in an algorithm that estimates endmembers, abundances, noise variances, and endmember uncertainty simultaneously. We compared SCM with current state-of-the-art algorithms on synthetic and real images. The results show that SCM can, in the main, provide more accurate endmembers and abundances. Moreover, the estimated uncertainty can serve as a prediction of endmember error under certain conditions.

Connected through hyperedge HE-1 (induced by source paper "improved deep spectral convolution network for hyperspectral unmixing with multinomial mixture kernel and endmember uncertainty"):
  with members:
    - "deep spectral convolution network for hyperspectral unmixing"
    - "a gaussian mixture model representation of endmember variability in hyperspectral unmixing"
    - "sliced wasserstein distance for learning gaussian mixture models"
    - "endnet sparse autoencoder network for endmember extraction and hyperspectral unmixing"
    - "improved training of wasserstein gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "deep spectral convolution network for hyperspectral unmixing"):
  with members:
    - "endnet sparse autoencoder network for endmember extraction and hyperspectral unmixing"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "instance normalization the missing ingredient for fast stylization"
    - "adam a method for stochastic optimization"
    - "spectral unmixing via data guided sparsity"

Connected through hyperedge HE-3 (induced by source paper "a gaussian mixture model representation of endmember variability in hyperspectral unmixing"):
  with members:
    - "fast fusion of multi band images based on solving a sylvester equation"

Connected through hyperedge HE-4 (induced by source paper "endnet sparse autoencoder network for endmember extraction and hyperspectral unmixing"):
  with members:
    - "cosine normalization using cosine similarity instead of dot product in neural networks"
    - "instance normalization the missing ingredient for fast stylization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "semi supervised learning with ladder networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"

Question: Please tell me which class the center node 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)