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: "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
  Abstract: Brain computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional Neural Networks (CNNs), which have been used in computer vision and speech recognition, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible. In this work we introduce EEGNet, a compact convolutional network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR). We show that EEGNet generalizes across paradigms better than the reference algorithms when only limited training data is available. We demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features. Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks, suggesting that the observed performances were not due to artifact or noise sources in the data.

Connected through hyperedge HE-1 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification"):
  with members:
    - "deep transfer learning for eeg based brain computer interface"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "prototypical networks for few shot learning"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
    - "matching networks for one shot learning"

Connected through hyperedge HE-2 (induced by source paper "eeg based drowsiness estimation for driving safety using deep q learning"):
  with members:
    - "network decoupling from regular to depthwise separable convolutions"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "learning representations from eeg with deep recurrent convolutional neural networks"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "motor imagery classification of single arm tasks using convolutional neural network based on feature refining"):
  with members:
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (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"
    - "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"
    - "deep learning for fatigue estimation on the basis of multimodal human machine interactions"

Connected through hyperedge HE-5 (induced by source paper "on the vulnerability of cnn classifiers in eeg based bcis"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial patch"
    - "adversarial spheres"
    - "audio adversarial examples targeted attacks on speech to text"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "eeg based user reaction time estimation using riemannian geometry features"
    - "online and offline domain adaptation for reducing bci calibration effort"

Connected through hyperedge HE-6 (induced by source paper "universal adversarial perturbations for cnn classifiers in eeg based bcis"):
  with members:
    - "on the vulnerability of cnn classifiers in eeg based bcis"
    - "universal adversarial perturbations for speech recognition systems"
    - "active learning for black box adversarial attacks in eeg based brain computer interfaces"
    - "adversarial patch"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "audio adversarial examples targeted attacks on speech to text"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "feature selection a data perspective"

Connected through hyperedge HE-7 (induced by source paper "active learning for black box adversarial attacks in eeg based brain computer interfaces"):
  with members:
    - "on the vulnerability of cnn classifiers in eeg based bcis"
    - "active learning for regression using greedy sampling"
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "affect estimation in 3d space using multi task active learning for regression"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "online and offline domain adaptation for reducing bci calibration effort"

Connected through hyperedge HE-8 (induced by source paper "adversarial deep learning in eeg biometrics"):
  with members:
    - "mindid person identification from brain waves through attention based recurrent neural network"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "controllable invariance through adversarial feature learning"
    - "in ear eeg biometrics for feasible and readily collectable real world person authentication"
    - "fader networks manipulating images by sliding attributes"
    - "xception deep learning with depthwise separable convolutions"
    - "deep feature learning for eeg recordings"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)