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: "effective spectral unmixing via robust representation and learning based sparsity"
  Abstract: Hyperspectral unmixing (HU) plays a fundamental role in a wide range of hyperspectral applications. It is still challenging due to the common presence of outlier channels and the large solution space. To address the above two issues, we propose a novel model by emphasizing both robust representation and learning-based sparsity. Specifically, we apply the $\ell_{2,1}$-norm to measure the representation error, preventing outlier channels from dominating our objective. In this way, the side effects of outlier channels are greatly relieved. Besides, we observe that the mixed level of each pixel varies over image grids. Based on this observation, we exploit a learning-based sparsity method to simultaneously learn the HU results and a sparse guidance map. Via this guidance map, the sparsity constraint in the $\ell_{p}\!\left(\!0\!<\! p\!\leq\!1\right)$-norm is adaptively imposed according to the learnt mixed level of each pixel. Compared with state-of-the-art methods, our model is better suited to the real situation, thus expected to achieve better HU results. The resulted objective is highly non-convex and non-smooth, and so it is hard to optimize. As a profound theoretical contribution, we propose an efficient algorithm to solve it. Meanwhile, the convergence proof and the computational complexity analysis are systematically provided. Extensive evaluations verify that our method is highly promising for the HU task---it achieves very accurate guidance maps and much better HU results compared with state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (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"
    - "spectral unmixing via data guided sparsity"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "comparing measures of sparsity"

Connected through hyperedge HE-3 (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"
    - "robust hyperspectral unmixing with correntropy based metric"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "group driven reinforcement learning for personalized mhealth intervention"):
  with members:
    - "cohesion based online actor critic reinforcement learning for mhealth intervention"
    - "structured sparse method for hyperspectral unmixing"
    - "10 000 times accelerated robust subset selection arss"
    - "robust hyperspectral unmixing with correntropy based metric"

Connected through hyperedge HE-5 (induced by source paper "spectral unmixing via data guided sparsity"):
  with members:
    - "structured sparse method for hyperspectral unmixing"
    - "10 000 times accelerated robust subset selection arss"
    - "non negative matrix factorization with sparseness constraints"
    - "non negative sparse coding"

Connected through hyperedge HE-6 (induced by source paper "10 000 times accelerated robust subset selection arss"):
  with members:
    - "an improved approximation algorithm for the column subset selection problem"

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)