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: "eeg based intention recognition from spatio temporal representations via cascade and parallel convolutional recurrent neural networks"
  Abstract: Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due to their convenient and portable instruments. Motor imagery EEG (MI-EEG) is a kind of most widely focused EEG signals, which reveals a subjects movement intentions without actual actions. Despite the extensive research of MI-EEG in recent years, it is still challenging to interpret EEG signals effectively due to the massive noises in EEG signals (e.g., low signal noise ratio and incomplete EEG signals), and difficulties in capturing the inconspicuous relationships between EEG signals and certain brain activities. Most existing works either only consider EEG as chain-like sequences neglecting complex dependencies between adjacent signals or performing simple temporal averaging over EEG sequences. In this paper, we introduce both cascade and parallel convolutional recurrent neural network models for precisely identifying human intended movements by effectively learning compositional spatio-temporal representations of raw EEG streams. The proposed models grasp the spatial correlations between physically neighboring EEG signals by converting the chain like EEG sequences into a 2D mesh like hierarchy. An LSTM based recurrent network is able to extract the subtle temporal dependencies of EEG data streams. Extensive experiments on a large-scale MI-EEG dataset (108 subjects, 3,145,160 EEG records) have demonstrated that both models achieve high accuracy near 98.3% and outperform a set of baseline methods and most recent deep learning based EEG recognition models, yielding a significant accuracy increase of 18% in the cross-subject validation scenario.

Connected through hyperedge HE-1 (induced by source paper "classification of hand movements from eeg using a deep attention based lstm network"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "lstm a search space odyssey"
    - "state of the art speech recognition with sequence to sequence models"
    - "regularizing and optimizing lstm language models"
    - "a critical review of recurrent neural networks for sequence learning"
    - "adam a method for stochastic optimization"
    - "automated classification of l r hand movement eeg signals using advanced feature extraction and machine learning"

Connected through hyperedge HE-2 (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"
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "intent recognition in smart living through deep recurrent neural networks"
    - "objective evaluation metrics for automatic classification of eeg events"

Connected through hyperedge HE-3 (induced by source paper "multi modality sensor data classification with selective attention"):
  with members:
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "intent recognition in smart living through deep recurrent neural networks"
    - "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"

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.HC(Human-Computer Interaction)