human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "know your mind adaptive brain signal classification with reinforced attentive convolutional neural networks"):
  Abstract: Electroencephalography (EEG) signals reflect activities on certain brain areas. Effective classification of time-varying EEG signals is still challenging. First, EEG signal processing and feature engineer- ing are time-consuming and highly rely on expert knowledge. In addition, most existing studies focus on domain-specific classifi- cation algorithms which may not be applicable to other domains. Moreover, the EEG signal usually has a low signal-to-noise ratio and can be easily corrupted. In this regard, we propose a generic EEG signal classification framework that accommodates a wide range of applications to address the aforementioned issues. The proposed framework develops a reinforced selective attention model to auto- matically choose the distinctive information among the raw EEG signals. A convolutional mapping operation is employed to dy- namically transform the selected information to an over-complete feature space, wherein implicit spatial dependency of EEG samples distribution is able to be uncovered. We demonstrate the effec- tiveness of the proposed framework using three representative scenarios: intention recognition with motor imagery EEG, person identification, and neurological diagnosis. Three widely used public datasets and a local dataset are used for our evaluation. The experi- ments show that our framework outperforms the state-of-the-art baselines and achieves the accuracy of more than 97% on all the datasets with low latency and good resilience of handling complex EEG signals across various domains. These results confirm the suit- ability of the proposed generic approach for a range of problems in the realm of Brain-Computer Interface applications.

Includes members:
  Member "a survey on deep learning based brain computer interface recent advances and new frontiers":
    also connected through other hyperedges:
      (none)
  Member "adversarial variational embedding for robust semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")
  Member "multi modality sensor data classification with selective attention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mdldroid a chainsgd reduce approach to mobile deep learning for personal mobile sensing")
  Member "automatic analysis of eegs using big data and hybrid deep learning architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "objective evaluation metrics for automatic classification of eeg events")
  Member "mindid person identification from brain waves through attention based recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepbrain towards personalized eeg interaction through attentional and embedded lstm learning")
  Member "eeg based intention recognition from spatio temporal representations via cascade and parallel convolutional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "classification of hand movements from eeg using a deep attention based lstm network")
  Member "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepbrain towards personalized eeg interaction through attentional and embedded lstm learning")
  Member "intent recognition in smart living through deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")

Question: Please tell me which class the source paper 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)