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: "feature representation in convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNNs) are powerful models that achieve impressive results for image classification. In addition, pre-trained CNNs are also useful for other computer vision tasks as generic feature extractors. This paper aims to gain insight into the feature aspect of CNN and demonstrate other uses of CNN features. Our results show that CNN feature maps can be used with Random Forests and SVM to yield classification results that outperforms the original CNN. A CNN that is less than optimal (e.g. not fully trained or overfitting) can also extract features for Random Forest/SVM that yield competitive classification accuracy. In contrast to the literature which uses the top-layer activations as feature representation of images for other tasks, using lower-layer features can yield better results for classification.

Connected through hyperedge HE-1 (induced by source paper "experimental evaluation of human motion prediction toward safe and efficient human robot collaboration"):
  with members:
    - "human motion prediction using semi adaptable neural networks"
    - "serocs safe and efficient robot collaborative systems for next generation intelligent industrial co robots"
    - "learning human motion models for long term predictions"

Connected through hyperedge HE-2 (induced by source paper "summarizing videos with attention"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "amnet memorability estimation with attention"
    - "learning video summarization using unpaired data"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "attention is all you need"
    - "video summarization with attention based encoder decoder networks"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-3 (induced by source paper "a combined cnn and lstm model for arabic sentiment analysis"):
  with members:
    - "improving sentiment analysis in arabic using word representation"
    - "gradascent at emoint 2017 character and word level recurrent neural network models for tweet emotion intensity detection"
    - "combining lexical features and a supervised learning approach for arabic sentiment analysis"
    - "arabic language sentiment analysis on health services"
    - "multichannel variable size convolution for sentence classification"
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "enriching word vectors with subword information"
    - "sentiment analysis for modern standard arabic and colloquial"

Connected through hyperedge HE-4 (induced by source paper "human motion prediction using semi adaptable neural networks"):
  with members:
    - "serocs safe and efficient robot collaborative systems for next generation intelligent industrial co robots"
    - "learning human motion models for long term predictions"
    - "cnn features off the shelf an astounding baseline for 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)