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 feature learning for eeg recordings"
  Abstract: We introduce and compare several strategies for learning discriminative features from electroencephalography (EEG) recordings using deep learning techniques. EEG data are generally only available in small quantities, they are high-dimensional with a poor signal-to-noise ratio, and there is considerable variability between individual subjects and recording sessions. Our proposed techniques specifically address these challenges for feature learning. Cross-trial encoding forces auto-encoders to focus on features that are stable across trials. Similarity-constraint encoders learn features that allow to distinguish between classes by demanding that two trials from the same class are more similar to each other than to trials from other classes. This tuple-based training approach is especially suitable for small datasets. Hydra-nets allow for separate processing pathways adapting to subsets of a dataset and thus combine the advantages of individual feature learning (better adaptation of early, low-level processing) with group model training (better generalization of higher-level processing in deeper layers). This way, models can, for instance, adapt to each subject individually to compensate for differences in spatial patterns due to anatomical differences or variance in electrode positions. The different techniques are evaluated using the publicly available OpenMIIR dataset of EEG recordings taken while participants listened to and imagined music.

Connected through hyperedge HE-1 (induced by source paper "deepbrain towards personalized eeg interaction through attentional and embedded lstm learning"):
  with members:
    - "mindid person identification from brain waves through attention based recurrent neural network"
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "adam a method for stochastic optimization"
    - "generating sequences with recurrent neural networks"
    - "multi dimensional recurrent neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "adversarial deep learning in eeg biometrics"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "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"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "recognition of advertisement emotions with application to computational advertising"):
  with members:
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "affect recognition in ads with application to computational advertising"
    - "learning transferable architectures for scalable image recognition"
    - "deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders"
    - "evaluating content centric vs user centric ad affect recognition"
    - "discovering gender differences in facial emotion recognition via implicit behavioral cues"
    - "object detection can be improved using human derived contextual expectations"
    - "dexpression deep convolutional neural network for expression recognition"

Connected through hyperedge HE-5 (induced by source paper "adversarial feature learning in brain interfacing an experimental study on eliminating drowsiness effects"):
  with members:
    - "adversarial deep learning in eeg biometrics"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "transfer learning in brain computer interfaces with adversarial variational autoencoders"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "controllable invariance through adversarial feature learning"
    - "fader networks manipulating images by sliding attributes"
    - "censoring representations with an adversary"
    - "learning representations from eeg with deep recurrent convolutional neural networks"

Connected through hyperedge HE-6 (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"
    - "learning robust features using deep learning for automatic seizure detection"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "focal onset seizure prediction using convolutional networks"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "adadelta an adaptive learning rate method"
    - "smote synthetic minority over sampling technique"
    - "identifying mislabeled training data"

Connected through hyperedge HE-8 (induced by source paper "training on the test set an analysis of spampinato et al 31"):
  with members:
    - "improved techniques for training gans"
    - "learning representations from eeg with deep recurrent convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "generalized fisher score for feature selection"

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.NE(Neural and Evolutionary Computing)