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: "learning robust features using deep learning for automatic seizure detection"
  Abstract: We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures. This is a challenging problem because seizure manifestations on EEG are extremely variable both inter- and intra-patient. By simultaneously capturing spectral, temporal and spatial information our recurrent convolutional neural network learns a general spatially invariant representation of a seizure. The proposed approach exceeds significantly previous results obtained on cross-patient classifiers both in terms of sensitivity and false positive rate. Furthermore, our model proves to be robust to missing channel and variable electrode montage.

Connected through hyperedge HE-1 (induced by source paper "temporal graph convolutional networks for automatic seizure detection"):
  with members:
    - "how powerful are graph neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "deep architectures for automated seizure detection in scalp eegs"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "temporal convolutional networks for action segmentation and detection"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "time guided high order attention model of longitudinal heterogeneous healthcare data"):
  with members:
    - "medical concept embedding with time aware attention"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "the use of autoencoders for discovering patient phenotypes"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "gram graph based attention model for healthcare representation learning"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "seizurenet multi spectral deep feature learning for seizure type classification"):
  with members:
    - "machine learning for seizure type classification setting the benchmark"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep belief networks used on high resolution multichannel electroencephalography data for seizure detection"
    - "densely connected convolutional networks"
    - "detecting epileptic seizures from eeg data using neural networks"

Connected through hyperedge HE-4 (induced by source paper "a novel independent rnn approach to classification of seizures against non seizures"):
  with members:
    - "subject selection on a riemannian manifold for unsupervised cross subject seizure detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "eegnet a compact convolutional neural network for eeg based brain computer interfaces"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "xception deep learning with depthwise separable convolutions"
    - "tensorflow a system for large scale machine learning"
    - "deep learning in neural networks"

Connected through hyperedge HE-6 (induced by source paper "compact convolutional neural networks for classification of asynchronous steady state visual evoked potentials"):
  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"
    - "a feasibility study on ssvep based interaction with motivating and immersive virtual and augmented reality"
    - "xception deep learning with depthwise separable convolutions"
    - "tensorflow a system for large scale machine learning"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

Connected through hyperedge HE-7 (induced by source paper "cost sensitive convolution based neural networks for imbalanced time series classification"):
  with members:
    - "lstm fully convolutional networks for time series classification"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "deep learning for time series analysis"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"
    - "earliness aware deep convolutional networks for early time series classification"
    - "cost sensitive learning of deep feature representations from imbalanced data"

Connected through hyperedge HE-8 (induced by source paper "hierarchical internal representation of spectral features in deep convolutional networks trained for eeg decoding"):
  with members:
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "deep learning in neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)