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: "mindid person identification from brain waves through attention based recurrent neural network"
  Abstract: Person identification technology recognizes individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, the state-of-the-art person identification systems have been shown to be vulnerable, e.g., contact lenses can trick iris recognition and fingerprint films can deceive fingerprint sensors. EEG (Electroencephalography)-based identification, which utilizes the users brainwave signals for identification and offers a more resilient solution, draw a lot of attention recently. However, the accuracy still requires improvement and very little work is focusing on the robustness and adaptability of the identification system. We propose MindID, an EEG-based biometric identification approach, achieves higher accuracy and better characteristics. At first, the EEG data patterns are analyzed and the results show that the Delta pattern contains the most distinctive information for user identification. Then the decomposed Delta pattern is fed into an attention-based Encoder-Decoder RNNs (Recurrent Neural Networks) structure which assigns varies attention weights to different EEG channels based on the channels importance. The discriminative representations learned from the attention-based RNN are used to recognize the user identification through a boosting classifier. The proposed approach is evaluated over 3 datasets (two local and one public). One local dataset (EID-M) is used for performance assessment and the result illustrate that our model achieves the accuracy of 0.982 which outperforms the baselines and the state-of-the-art. Another local dataset (EID-S) and a public dataset (EEG-S) are utilized to demonstrate the robustness and adaptability, respectively. The results indicate that the proposed approach has the potential to be largely deployment in practice environment.

Connected through hyperedge HE-1 (induced by source paper "deepbrain towards personalized eeg interaction through attentional and embedded lstm learning"):
  with members:
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "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 "adversarial deep learning in eeg biometrics"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "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"

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

Connected through hyperedge HE-4 (induced by source paper "deepkey an eeg and gait based dual authentication system"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"

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)