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: "on deep representation learning from noisy web images"
  Abstract: The keep-growing content of Web images may be the next important data source to scale up deep neural networks, which recently obtained a great success in the ImageNet classification challenge and related tasks. This prospect, however, has not been validated on convolutional networks (convnet) -- one of best performing deep models -- because of their supervised regime. While unsupervised alternatives are not so good as convnet in generalizing the learned model to new domains, we use convnet to leverage semi-supervised representation learning. Our approach is to use massive amounts of unlabeled and noisy Web images to train convnets as general feature detectors despite challenges coming from data such as high level of mislabeled data, outliers, and data biases. Extensive experiments are conducted at several data scales, different network architectures, and data reranking techniques. The learned representations are evaluated on nine public datasets of various topics. The best results obtained by our convnets, trained on 3.14 million Web images, outperform AlexNet trained on 1.2 million clean images of ILSVRC 2012 and is closing the gap with VGG-16. These prominent results suggest a budget solution to use deep learning in practice and motivate more research in semi-supervised representation learning.

Connected through hyperedge HE-1 (induced by source paper "automatic dataset augmentation"):
  with members:
    - "learning deep visual object models from noisy web data how to make it work"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "bag of tricks for efficient text classification"
    - "yfcc100m the new data in multimedia research"
    - "deep residual learning for image recognition"
    - "the unreasonable effectiveness of noisy data for fine grained recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "automatic attribute discovery with neural activations"):
  with members:
    - "a deep learning based fast image saliency detection algorithm"
    - "understanding neural networks through deep visualization"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "deep carving discovering visual attributes by carving deep neural nets"
    - "visual affect around the world a large scale multilingual visual sentiment ontology"
    - "automatic concept discovery from parallel text and visual corpora"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"

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)