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: "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
  Abstract: An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.

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"
    - "deep feature learning for eeg recordings"
    - "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 "speak your mind towards imagined speech recognition with hierarchical deep learning"):
  with members:
    - "hierarchical deep feature learning for decoding imagined speech from eeg"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "neural networks based eeg speech models"
    - "xgboost a scalable tree boosting system"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "emerging natural user interfaces in mobile computing a bottoms up survey"):
  with members:
    - "adaptive emg based hand gesture recognition using hyperdimensional computing"
    - "interaction methods for smart glasses a survey"
    - "a survey on haptic technologies for mobile augmented reality"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-5 (induced by source paper "deep learning the eeg manifold for phonological categorization from active thoughts"):
  with members:
    - "hierarchical deep feature learning for decoding imagined speech from eeg"
    - "neural networks based eeg speech models"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-6 (induced by source paper "deep neural network hyperparameter optimization with orthogonal array tuning"):
  with members:
    - "intent recognition in smart living through deep recurrent neural networks"

Connected through hyperedge HE-7 (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"
    - "intent recognition in smart living through deep recurrent neural networks"
    - "objective evaluation metrics for automatic classification of eeg events"

Connected through hyperedge HE-8 (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"
    - "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)