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: "structured sparse method for hyperspectral unmixing"
  Abstract: Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method from the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.

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 "clustered multitask nonnegative matrix factorization for spectral unmixing of hyperspectral data"):
  with members:
    - "sparsity constrained distributed unmixing of hyperspectral data"
    - "hyperspectral unmixing based on clustered multitask networks"
    - "spectral unmixing of hyperspectral imagery using multilayer nmf"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "hyperspectral data unmixing using gnmf method and sparseness constraint"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-3 (induced by source paper "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"):
  with members:
    - "joint progressive learning from high dimensional data for multi label classification"
    - "dynamical spectral unmixing of multitemporal hyperspectral images"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"

Connected through hyperedge HE-4 (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-5 (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"

Connected through hyperedge HE-6 (induced by source paper "hyperspectral unmixing ground truth labeling datasets benchmark performances and survey"):
  with members:
    - "cohesion based online actor critic reinforcement learning for mhealth intervention"
    - "unsupervised nonlinear spectral unmixing based on a multilinear mixing model"
    - "effective warm start for the online actor critic reinforcement learning based mhealth intervention"
    - "low rank representation on riemannian manifold of square root densities"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "spectral unmixing via data guided sparsity"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "comparing measures of sparsity"

Connected through hyperedge HE-7 (induced by source paper "robust contextual bandit via the capped ell_ 2 norm"):
  with members:
    - "cohesion based online actor critic reinforcement learning for mhealth intervention"
    - "latent contextual bandits and their application to personalized recommendations for new users"
    - "10 000 times accelerated robust subset selection arss"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "group driven reinforcement learning for personalized mhealth intervention"):
  with members:
    - "cohesion based online actor critic reinforcement learning for mhealth intervention"
    - "10 000 times accelerated robust subset selection arss"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "robust hyperspectral unmixing with correntropy based metric"

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)