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 "adversarial feature learning in brain interfacing an experimental study on eliminating drowsiness effects"):
  Abstract: Across- and within-recording variabilities in electroencephalographic (EEG) activity is a major limitation in EEG-based brain-computer interfaces (BCIs). Specifically, gradual changes in fatigue and vigilance levels during long EEG recording durations and BCI system usage bring along significant fluctuations in BCI performances even when these systems are calibrated daily. We address this in an experimental offline study from EEG-based BCI speller usage data acquired for one hour duration. As the main part of our methodological approach, we propose the concept of adversarial invariant feature learning for BCIs as a regularization approach on recently expanding EEG deep learning architectures, to learn nuisance-invariant discriminative features. We empirically demonstrate the feasibility of adversarial feature learning on eliminating drowsiness effects from event related EEG activity features, by using temporal recording block ordering as the source of drowsiness variability.

Includes members:
  Member "adversarial deep learning in eeg biometrics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "information theoretic feature transformation learning for brain interfaces")
  Member "eegnet a compact convolutional neural network for eeg based brain computer interfaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")
  Member "transfer learning in brain computer interfaces with adversarial variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "information theoretic feature transformation learning for brain interfaces")
  Member "deep learning with convolutional neural networks for eeg decoding and visualization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "collaborative inference for efficient remote monitoring")
  Member "controllable invariance through adversarial feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "fader networks manipulating images by sliding attributes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "deep feature learning for eeg recordings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepbrain towards personalized eeg interaction through attentional and embedded lstm learning")
  Member "censoring representations with an adversary":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithmic fairness")

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)