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: "on the vulnerability of cnn classifiers in eeg based bcis"
  Abstract: Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG) based brain-computer interface (BCI), where multiple convolutional neural network (CNN) models have been proposed for EEG classification. However, it has been found that deep learning models can be easily fooled with adversarial examples, which are normal examples with small deliberate perturbations. This paper proposes an unsupervised fast gradient sign method (UFGSM) to attack three popular CNN classifiers in BCIs, and demonstrates its effectiveness. We also verify the transferability of adversarial examples in BCIs, which means we can perform attacks even without knowing the architecture and parameters of the target models, or the datasets they were trained on. To our knowledge, this is the first study on the vulnerability of CNN classifiers in EEG-based BCIs, and hopefully will trigger more attention on the security of BCI systems.

Connected through hyperedge HE-1 (induced by source paper "tiny noise can make an eeg based brain computer interface speller output anything"):
  with members:
    - "adversarial patch"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial examples for evaluating reading comprehension systems"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "universal adversarial perturbations"
    - "delving into transferable adversarial examples and black box attacks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-2 (induced by source paper "universal adversarial perturbations for cnn classifiers in eeg based bcis"):
  with members:
    - "universal adversarial perturbations for speech recognition systems"
    - "active learning for black box adversarial attacks in eeg based brain computer interfaces"
    - "adversarial patch"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "audio adversarial examples targeted attacks on speech to text"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "feature selection a data perspective"

Connected through hyperedge HE-3 (induced by source paper "active learning for black box adversarial attacks in eeg based brain computer interfaces"):
  with members:
    - "active learning for regression using greedy sampling"
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "affect estimation in 3d space using multi task active learning for regression"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "online and offline domain adaptation for reducing bci calibration effort"

Connected through hyperedge HE-4 (induced by source paper "white box target attack for eeg based bci regression problems"):
  with members:
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "eeg based user reaction time estimation using riemannian geometry features"
    - "spatial filtering for eeg based regression problems in brain computer interface bci"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-5 (induced by source paper "manifold embedded knowledge transfer for brain computer interfaces"):
  with members:
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
    - "return of frustratingly easy domain adaptation"

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)