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: "10 000 times accelerated robust subset selection arss"
  Abstract: Subset selection from massive data with noised information is increasingly popular for various applications. This problem is still highly challenging as current methods are generally slow in speed and sensitive to outliers. To address the above two issues, we propose an accelerated robust subset selection (ARSS) method. Specifically in the subset selection area, this is the first attempt to employ the $\ell_{p}(0<p\leq1)$-norm based measure for the representation loss, preventing large errors from dominating our objective. As a result, the robustness against outlier elements is greatly enhanced. Actually, data size is generally much larger than feature length, i.e. $N\gg L$. Based on this observation, we propose a speedup solver (via ALM and equivalent derivations) to highly reduce the computational cost, theoretically from $O(N^{4})$ to $O(N{}^{2}L)$. Extensive experiments on ten benchmark datasets verify that our method not only outperforms state of the art methods, but also runs 10,000+ times faster than the most related method.

Connected through hyperedge HE-1 (induced by source paper "computer aided detection of squamous carcinoma of the cervix in whole slide images"):
  with members:
    - "deep learning framework for multi class breast cancer histology image classification"
    - "cancer metastasis detection with neural conditional random field"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "recent advances in the applications of convolutional neural networks to medical image contour detection"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "deep retinal image understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identifying metastases in sentinel lymph nodes with deep convolutional neural networks"

Connected through hyperedge HE-2 (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"
    - "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-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"
    - "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-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"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "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"
    - "effective spectral unmixing via robust representation and learning based sparsity"
    - "non negative matrix factorization with sparseness constraints"
    - "non negative sparse coding"

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.LG(Machine Learning)