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 learning with convolutional neural networks for decoding and visualization of eeg pathology"
  Abstract: We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet architectures recently shown to decode task-related information from EEG at least as well as established algorithms designed for this purpose. In decoding EEG pathology, both ConvNets reached substantially better accuracies (about 6% better, ~85% vs. ~79%) than the only published result for this dataset, and were still better when using only 1 minute of each recording for training and only six seconds of each recording for testing. We used automated methods to optimize architectural hyperparameters and found intriguingly different ConvNet architectures, e.g., with max pooling as the only nonlinearity. Visualizations of the ConvNet decoding behavior showed that they used spectral power changes in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside other features, consistent with expectations derived from spectral analysis of the EEG data and from the textual medical reports. Analysis of the textual medical reports also highlighted the potential for accuracy increases by integrating contextual information, such as the age of subjects. In summary, the ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers"):
  with members:
    - "deep learning based electroencephalography analysis a systematic review"
    - "adversarial variational embedding for robust semi supervised learning"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "an automated system for epilepsy detection using eeg brain signals based on deep learning approach"
    - "on the classification of ssvep based dry eeg signals via convolutional neural networks"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "compact convolutional neural networks for classification of asynchronous steady state visual evoked potentials"
    - "hierarchical internal representation of spectral features in deep convolutional networks trained for eeg decoding"

Connected through hyperedge HE-2 (induced by source paper "adversarial variational embedding for robust semi supervised learning"):
  with members:
    - "multi modality sensor data classification with selective attention"
    - "interpretable parallel recurrent neural networks with convolutional attentions for multi modality activity modeling"
    - "neural factorization machines for sparse predictive analytics"
    - "cvae gan fine grained image generation through asymmetric training"
    - "objective evaluation metrics for automatic classification of eeg events"
    - "variational autoencoders for semi supervised text classification"
    - "improved techniques for training gans"
    - "an uncertain future forecasting from static images using variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "applying transfer learning to deep learned models for eeg analysis"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "unsupervised end to end learning for deformable medical image registration"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deep learning in neural networks"
    - "how transferable are features in deep neural networks"
    - "imagenet large scale visual recognition challenge"
    - "transfer learning for video recognition with scarce training data for deep convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "deep invertible networks for eeg based brain signal decoding"):
  with members:
    - "deep learning with convolutional neural networks for eeg decoding and visualization"

Connected through hyperedge HE-5 (induced by source paper "igloo slicing the features space to represent long sequences"):
  with members:
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "simple recurrent units for highly parallelizable recurrence"
    - "kronecker recurrent units"
    - "recurrent additive networks"
    - "dilated recurrent neural networks"
    - "quasi recurrent neural networks"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-6 (induced by source paper "intracranial error detection via deep learning"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "hierarchical internal representation of spectral features in deep convolutional networks trained for eeg decoding"
    - "deep classification of epileptic signals"
    - "the signature of robot action success in eeg signals of a human observer decoding and visualization using deep convolutional neural networks"
    - "decoupled weight decay regularization"
    - "deep transfer learning for error decoding from non invasive eeg"
    - "ajile movement prediction multimodal deep learning for natural human neural recordings and video"
    - "sgdr stochastic gradient descent with restarts"

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)