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: "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
  Abstract: In this paper, a graph-based nonlocal total variation method is proposed for unsupervised classification of hyperspectral images (HSI). The variational problem is solved by the primal-dual hybrid gradient algorithm. By squaring the labeling function and using a stable simplex clustering routine, an unsupervised clustering method with random initialization can be implemented. The effectiveness of this proposed algorithm is illustrated on both synthetic and real-world HSI, and numerical results show that the proposed algorithm outperforms other standard unsupervised clustering methods, such as spherical    $K$   -means, nonnegative matrix factorization, and the graph-based Merriman–Bence–Osher scheme.

Connected through hyperedge HE-1 (induced by source paper "stochastic block models are a discrete surface tension"):
  with members:
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "uncertainty quantification in graph based classification of high dimensional data"
    - "efficient method for estimating the number of communities in a network"
    - "block models and personalized pagerank"
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"

Connected through hyperedge HE-2 (induced by source paper "punch positive unlabelled classification based information retrieval in hyperspectral images"):
  with members:
    - "ladder networks for semi supervised hyperspectral image classification"
    - "positive unlabeled learning with non negative risk estimator"
    - "bass net band adaptive spectral spatial feature learning neural network for hyperspectral image classification"
    - "generative adversarial positive unlabelled learning"
    - "learning to match using local and distributed representations of text for web search"
    - "deep learning in neural networks"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

Connected through hyperedge HE-3 (induced by source paper "spectral spatial diffusion geometry for hyperspectral image clustering"):
  with members:
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "experiments with random projection"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-4 (induced by source paper "learning by active nonlinear diffusion"):
  with members:
    - "spectral spatial diffusion geometry for hyperspectral image clustering"
    - "unsupervised clustering and active learning of hyperspectral images with nonlinear diffusion"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"

Connected through hyperedge HE-5 (induced by source paper "hyperspectral image classification with markov random fields and a convolutional neural network"):
  with members:
    - "clearing the skies a deep network architecture for single image rain removal"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "image super resolution using deep convolutional networks"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "structured priors for sparse representation based hyperspectral image classification"
    - "spatial aware dictionary learning for hyperspectral image classification"

Connected through hyperedge HE-6 (induced by source paper "unsupervised clustering and active learning of hyperspectral images with nonlinear diffusion"):
  with members:
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "experiments with random projection"
    - "sparse subspace clustering algorithm theory and applications"
    - "a tutorial on spectral clustering"

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)