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: "deep convolutional neural networks for interpretable analysis of eeg sleep stage scoring"
  Abstract: Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals, which is a tedious visual task requiring the workload of highly trained professionals. Consequently, research efforts to purse for an automatic stage scoring based on machine learning techniques have been carried out over the last years. In this work, we resort to multitaper spectral analysis to create visually interpretable images of sleep patterns from EEG signals as inputs to a deep convolutional network trained to solve visual recognition tasks. As a working example of transfer learning, a system able to accurately classify sleep stages in new unseen patients is presented. Evaluations in a widely-used publicly available dataset favourably compare to state-of-the-art results, while providing a framework for visual interpretation of outcomes.

Connected through hyperedge HE-1 (induced by source paper "lucid a practical lightweight deep learning solution for ddos attack detection"):
  with members:
    - "a practical approach to detection of distributed denial of service attacks using a hybrid detection method"
    - "on evaluating adversarial robustness"
    - "understanding convolutional neural networks for text classification"
    - "automated vulnerability detection in source code using deep representation learning"
    - "enorm a framework for edge node resource management"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "u time a fully convolutional network for time series segmentation applied to sleep staging"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "joint classification and prediction cnn framework for automatic sleep stage classification"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "sleepnet automated sleep staging system via deep learning"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "cnn is all you need"

Connected through hyperedge HE-3 (induced by source paper "intra and inter epoch temporal context network iitnet for automatic sleep stage scoring"):
  with members:
    - "joint classification and prediction cnn framework for automatic sleep stage classification"
    - "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"
    - "sleepnet automated sleep staging system via deep learning"
    - "neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy"
    - "mask r cnn"
    - "identity mappings in deep residual networks"
    - "hierarchical question image co attention for visual question answering"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"

Connected through hyperedge HE-4 (induced by source paper "end to end sleep staging with raw single channel eeg using deep residual convnets"):
  with members:
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "identity mappings in deep residual networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms"):
  with members:
    - "sleepnet automated sleep staging system via deep learning"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale 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)