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: "adversarial deep learning in eeg biometrics"
  Abstract: Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG data. We address this problem from an invariant representation-learning perspective. We propose an adversarial inference approach to extend such deep learning models to learn session-invariant person-discriminative representations that can provide robustness in terms of longitudinal usability. Using adversarial learning within a deep convolutional network, we empirically assess and show improvements with our approach based on longitudinally collected EEG data for person identification from half-second EEG epochs.

Connected through hyperedge HE-1 (induced by source paper "information theoretic feature transformation learning for brain interfaces"):
  with members:
    - "hierarchical graphical models for context aware hybrid brain machine interfaces"
    - "multivariate extension of matrix based renyi s alpha order entropy functional"
    - "transfer learning in brain computer interfaces with adversarial variational autoencoders"
    - "estimating mutual information for discrete continuous mixtures"
    - "information theoretic semi supervised metric learning via entropy regularization"

Connected through hyperedge HE-2 (induced by source paper "adversarial feature learning in brain interfacing an experimental study on eliminating drowsiness effects"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "transfer learning in brain computer interfaces with adversarial variational autoencoders"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "controllable invariance through adversarial feature learning"
    - "fader networks manipulating images by sliding attributes"
    - "deep feature learning for eeg recordings"
    - "censoring representations with an adversary"
    - "learning representations from eeg with deep recurrent convolutional neural networks"

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.LG(Machine Learning)