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: "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
  Abstract: In this paper, we design a fast hierarchical clustering algorithm for high-resolution hyperspectral images (HSI). At the core of the algorithm, a new rank-two nonnegative matrix factorization (NMF) algorithm is used to split the clusters, which is motivated by convex geometry concepts. The method starts with a single cluster containing all pixels and, at each step, performs the following: 1) selects a cluster in such a way that the error at the next step is minimized and 2) splits the selected cluster into two disjoint clusters using rank-two NMF in such a way that the clusters are well balanced and stable. The proposed method can also be used as an endmember extraction algorithm in the presence of pure pixels. The effectiveness of this approach is illustrated on several synthetic and real-world HSIs and is shown to outperform standard clustering techniques such as k-means, spherical k-means, and standard NMF.

Connected through hyperedge HE-1 (induced by source paper "generalized separable nonnegative matrix factorization"):
  with members:
    - "on identifiability of nonnegative matrix factorization"
    - "learning the information divergence"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning topic models going beyond svd"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-2 (induced by source paper "spectral spatial diffusion geometry for hyperspectral image clustering"):
  with members:
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
    - "experiments with random projection"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-3 (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"
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"

Connected through hyperedge HE-4 (induced by source paper "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs"):
  with members:
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "efficient sparse subspace clustering by nearest neighbour filtering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "provable deterministic leverage score sampling"
    - "robust and efficient subspace segmentation via least squares regression"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"

Connected through hyperedge HE-5 (induced by source paper "unsupervised clustering and active learning of hyperspectral images with nonlinear diffusion"):
  with members:
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
    - "experiments with random projection"
    - "sparse subspace clustering algorithm theory and applications"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "nonlinear unsupervised clustering of hyperspectral images with applications to anomaly detection and active learning"):
  with members:
    - "experiments with random projection"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-7 (induced by source paper "greedy algorithms for cone constrained optimization with convergence guarantees"):
  with members:
    - "a hybrid algorithm for convex semidefinite optimization"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-8 (induced by source paper "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"):
  with members:
    - "structured sparse method for hyperspectral unmixing"
    - "building a balanced k d tree in o kn log n time"

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)