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: "multiple kernel sparse representations for supervised and unsupervised learning"
  Abstract: In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "convolutional sparse kernel network for unsupervised medical image analysis"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "a survey on deep learning in medical image analysis"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "unsupervised deep feature extraction for remote sensing image classification"

Connected through hyperedge HE-2 (induced by source paper "unsupervised feature learning with k means and an ensemble of deep convolutional neural networks for medical image classification"):
  with members:
    - "convolutional sparse kernel network for unsupervised medical image analysis"
    - "unsupervised segmentation of 3d medical images based on clustering and deep representation learning"
    - "deep clustering for unsupervised learning of visual features"
    - "joint unsupervised learning of deep representations and image clusters"
    - "deep residual learning for image recognition"
    - "unsupervised deep embedding for clustering analysis"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional kernel networks"

Connected through hyperedge HE-3 (induced by source paper "interpretable multiple kernel prototype learning for discriminative representation and feature selection"):
  with members:
    - "confident kernel sparse coding and dictionary learning"
    - "a review of learning vector quantization classifiers"
    - "a kernel for time series based on global alignments"

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)