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: "robust hyperspectral unmixing with correntropy based metric"
  Abstract: Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The problem of hyperspectral unmixing has proven to be a difficult task in unsupervised work settings where the endmembers and abundances are both unknown. What is more, this task becomes more challenging in the case that the spectral bands are degraded with noise. This paper presents a robust model for unsupervised hyperspectral unmixing. Specifically, our model is developed with the correntropy based metric where the non-negative constraints on both endmembers and abundances are imposed to keep physical significance. In addition, a sparsity prior is explicitly formulated to constrain the distribution of the abundances of each endmember. To solve our model, a half-quadratic optimization technique is developed to convert the original complex optimization problem into an iteratively re-weighted NMF with sparsity constraints. As a result, the optimization of our model can adaptively assign small weights to noisy bands and give more emphasis on noise-free bands. In addition, with sparsity constraints, our model can naturally generate sparse abundances. Experiments on synthetic and real data demonstrate the effectiveness of our model in comparison to the related state-of-the-art unmixing models.

Connected through hyperedge HE-1 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise"):
  with members:
    - "learning without concentration"
    - "consistency analysis of an empirical minimum error entropy algorithm"
    - "learning theory approach to minimum error entropy criterion"

Connected through hyperedge HE-2 (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"
    - "structured sparse method for hyperspectral unmixing"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"

Connected through hyperedge HE-3 (induced by source paper "material based object tracking in hyperspectral videos benchmark and algorithms"):
  with members:
    - "context aware deep feature compression for high speed visual tracking"
    - "object tracking in hyperspectral videos with convolutional features and kernelized correlation filter"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "geometry informed material recognition"

Connected through hyperedge HE-4 (induced by source paper "robust actor critic contextual bandit for mobile health mhealth interventions"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "name disambiguation in anonymized graphs using network embedding"
    - "effective warm start for the online actor critic reinforcement learning based mhealth intervention"
    - "recurrent reinforcement learning a hybrid approach"
    - "median filtering is equivalent to sorting"
    - "10 000 times accelerated robust subset selection arss"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-5 (induced by source paper "multi view surveillance video summarization via joint embedding and sparse optimization"):
  with members:
    - "collaborative summarization of topic related videos"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "video summarization in a multi view camera network"
    - "learning from multiple sources for video summarisation"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "active object localization with deep reinforcement learning"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "collaborative summarization of topic related videos"):
  with members:
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"
    - "video summarization in a multi view camera network"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (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"
    - "structured sparse method for hyperspectral unmixing"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "spectral unmixing via data guided sparsity"
    - "comparing measures of sparsity"

Connected through hyperedge HE-8 (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"
    - "structured sparse method for hyperspectral unmixing"
    - "10 000 times accelerated robust subset selection arss"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "a contextual bandit approach to personalized news article recommendation"

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)