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: "spectral unmixing via data guided sparsity"
  Abstract: Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization, and understanding. From an unsupervised learning perspective, this problem is very challenging—both the spectral bases and their composite percentages are unknown, making the solution space too large. To reduce the solution space, many approaches have been proposed by exploiting various priors. In practice, these priors would easily lead to some unsuitable solution. This is because they are achieved by applying an identical strength of constraints to all the factors, which does not hold in practice. To overcome this limitation, we propose a novel sparsity-based method by learning a data-guided map (DgMap) to describe the individual mixed level of each pixel. Through this DgMap, the    $\ell _{p}\left (0   constraint is applied in an adaptive manner. Such implementation not only meets the practical situation, but also guides the spectral bases toward the pixels under highly sparse constraint. What is more, an elegant optimization scheme as well as its convergence proof have been provided in this paper. Extensive experiments on several datasets also demonstrate that the DgMap is feasible, and high quality unmixing results could be obtained by our method.

Connected through hyperedge HE-1 (induced by source paper "low rank tensor grid for image completion"):
  with members:
    - "provable model for tensor ring completion"
    - "low rank tensor completion for multiway visual data"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient low rank tensor ring completion"
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "an iterative reweighted method for tucker decomposition of incomplete tensors"
    - "tensor completion by alternating minimization under the tensor train tt model"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"

Connected through hyperedge HE-2 (induced by source paper "robust tensor completion using transformed tensor svd"):
  with members:
    - "exact tensor completion from sparsely corrupted observations via convex optimization"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "parallel matrix factorization for low rank tensor completion"
    - "incoherence optimal matrix completion"
    - "exact tensor completion using t svd"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

Connected through hyperedge HE-3 (induced by source paper "algorithms and comparisons of nonnegative matrix factorizations with volume regularization for hyperspectral unmixing"):
  with members:
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-4 (induced by source paper "image smoothing via unsupervised learning"):
  with members:
    - "stereoscopic neural style transfer"
    - "deep exemplar based colorization"
    - "deep image prior"
    - "semi global weighted least squares in image filtering"
    - "deep texture and structure aware filtering network for image smoothing"
    - "deep bilateral learning for real time image enhancement"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "a generic deep architecture for single image reflection removal and image smoothing"

Connected through hyperedge HE-5 (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"
    - "a spatial compositional model for linear unmixing and endmember uncertainty estimation"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (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"
    - "a spatial compositional model for linear unmixing and endmember uncertainty estimation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "on statistical learning of simplices unmixing problem revisited"):
  with members:
    - "efficient learning of simplices"

Connected through hyperedge HE-8 (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"
    - "a spatial compositional model for linear unmixing and endmember uncertainty estimation"
    - "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"

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)