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 "intracranial error detection via deep learning"):
  Abstract: Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated for use in intracranial EEG. We employed convolutional neural networks (CNNs) to classify and characterize the error-related brain response as measured in 24 intracranial EEG recordings. Decoding accuracies of CNNs were significantly higher than those of a regularized linear discriminant analysis. Using time-resolved deep decoding, it was possible to classify errors in various regions in the human brain, and further to decode errors over 200 ms before the actual erroneous button press, e.g., in the precentral gyrus. Moreover, deeper networks performed better than shallower networks in distinguishing correct from error trials in all-channel decoding. In single recordings, up to 100 % decoding accuracy was achieved. Visualization of the networks' learned features indicated that multivariate decoding on an ensemble of channels yields related, albeit non-redundant information compared to single-channel decoding. In summary, here we show the usefulness of deep learning for both intracranial error decoding and mapping of the spatio-temporal structure of the human error processing network.

Includes members:
  Member "eegnet a compact convolutional neural network for eeg based brain computer interfaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")
  Member "hierarchical internal representation of spectral features in deep convolutional networks trained for eeg decoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")
  Member "deep classification of epileptic signals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "residual deep convolutional neural network for eeg signal classification in epilepsy")
  Member "the signature of robot action success in eeg signals of a human observer decoding and visualization using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")
  Member "deep learning with convolutional neural networks for decoding and visualization of eeg pathology":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")
  Member "decoupled weight decay regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep transfer learning for error decoding from non invasive eeg":
    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")
  Member "ajile movement prediction multimodal deep learning for natural human neural recordings and video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reconstructing speech stimuli from human auditory cortex activity using a wavenet approach")

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.LG(Machine Learning)