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: "intent recognition in smart living through deep recurrent neural networks"
  Abstract: Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time- consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects' intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided by PhysioNet and achieves the accuracy of 0.9553 on the intent recognition. The applicability of our proposed model is further demonstrated by two use cases of smart living (assisted living with robotics and home automation).

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 "deep neural network hyperparameter optimization with orthogonal array tuning"):
  with members:
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"

Connected through hyperedge HE-3 (induced by source paper "know your mind adaptive brain signal classification with reinforced attentive convolutional neural networks"):
  with members:
    - "a survey on deep learning based brain computer interface recent advances and new frontiers"
    - "adversarial variational embedding for robust semi supervised learning"
    - "multi modality sensor data classification with selective attention"
    - "automatic analysis of eegs using big data and hybrid deep learning architectures"
    - "mindid person identification from brain waves through attention based recurrent neural network"
    - "eeg based intention recognition from spatio temporal representations via cascade and parallel convolutional recurrent neural networks"
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "objective evaluation metrics for automatic classification of eeg events"

Connected through hyperedge HE-4 (induced by source paper "multi modality sensor data classification with selective attention"):
  with members:
    - "eeg based intention recognition from spatio temporal representations via cascade and parallel convolutional recurrent neural networks"
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "dueling network architectures for deep reinforcement learning"
    - "learning to diagnose with lstm recurrent neural networks"
    - "lstm based deep learning models for non factoid answer selection"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "internet of things meets brain computer interface a unified deep learning framework for enabling human thing cognitive interactivity"):
  with members:
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "in ear eeg biometrics for feasible and readily collectable real world person authentication"
    - "dueling network architectures for deep reinforcement learning"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "brain2object printing your mind from brain signals with spatial correlation embedding"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "multiclass common spatial pattern for eeg based brain computer interface with adaptive learning classifier"
    - "deep transfer learning for error decoding from non invasive eeg"
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "feature learning from incomplete eeg with denoising autoencoder"

Connected through hyperedge HE-7 (induced by source paper "context aware human intent inference for improving human machine cooperation"):
  with members:
    - "multi person brain activity recognition via comprehensive eeg signal analysis"
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"

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.CY(Computers and Society)