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: "feature learning from incomplete eeg with denoising autoencoder"
  Abstract: An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI). A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair. The effectiveness of the BCI depends on the performance in decoding the EEG. Usually, the EEG is contaminated by different kinds of artefacts (e.g., electromyogram (EMG), background activity), which leads to a low decoding performance. A number of filtering methods can be utilized to remove or weaken the effects of artefacts, but they generally fail when the EEG contains extreme artefacts. In such cases, the most common approach is to discard the whole data segment containing extreme artefacts. This causes the fatal drawback that the BCI cannot output decoding results during that time. In order to solve this problem, we employ the Lomb-Scargle periodogram to estimate the spectral power from incomplete EEG (after removing only parts contaminated by artefacts), and Denoising Autoencoder (DAE) for learning. The proposed method is evaluated with motor imagery EEG data. The results show that our method can successfully decode incomplete EEG to good effect.

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 "no free lunch but a cheaper supper a general framework for streaming anomaly detection"):
  with members:
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "a meta analysis of the anomaly detection problem"
    - "systematic construction of anomaly detection benchmarks from real data"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"

Connected through hyperedge HE-3 (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"
    - "intent recognition in smart living through deep recurrent neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "multimodal classification with deep convolutional recurrent neural networks for electroencephalography"):
  with members:
    - "deep learning in bioinformatics"
    - "deep learning in neural networks"
    - "beyond short snippets deep networks for video classification"
    - "learning representations from eeg with deep recurrent convolutional neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "canonical polyadic decomposition with auxiliary information for brain computer interface"):
  with members:
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"

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.CV(Computer Vision and Pattern Recognition)