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 "universal adversarial perturbations for cnn classifiers in eeg based bcis"):
  Abstract: Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been found vulnerable to universal adversarial perturbations (UAPs), which are small and example-independent, yet powerful enough to degrade the performance of a CNN model, when added to a benign example. This paper proposes a novel total loss minimization (TLM) approach to generate UAPs for EEG-based BCIs. Experimental results demonstrate the effectiveness of TLM on three popular CNN classifiers for both target and non-target attacks. We also verify the transferability of UAPs in EEG-based BCI systems. To our knowledge, this is the first study on UAPs of CNN classifiers in EEG-based BCIs, and also the first study on UAPs for target attacks. UAPs are easy to construct, and can attack BCIs in real-time, exposing a critical security concern of BCIs.

Includes members:
  Member "on the vulnerability of cnn classifiers in eeg based bcis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tiny noise can make an eeg based brain computer interface speller output anything")
  Member "universal adversarial perturbations for speech recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial deepfakes evaluating vulnerability of deepfake detectors to adversarial examples")
  Member "active learning for black box adversarial attacks in eeg based brain computer interfaces":
    also connected through other hyperedges:
      (none)
  Member "adversarial patch":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "eegnet a compact convolutional neural network for eeg based brain computer interfaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")
  Member "generalizable data free objective for crafting universal adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "frequency tuned universal adversarial attacks")
  Member "audio adversarial examples targeted attacks on speech to text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "deep learning with convolutional neural networks for eeg decoding and visualization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "collaborative inference for efficient remote monitoring")

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)