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 "active learning for black box adversarial attacks in eeg based brain computer interfaces"):
  Abstract: Deep learning has made significant breakthroughs in many fields, including electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, deep learning models are vulnerable to adversarial attacks, in which deliberately designed small perturbations are added to the benign input samples to fool the deep learning model and degrade its performance. This paper considers transferability-based black-box attacks, where the attacker trains a substitute model to approximate the target model, and then generates adversarial examples from the substitute model to attack the target model. Learning a good substitute model is critical to the success of these attacks, but it requires a large number of queries to the target model. We propose a novel framework which uses query synthesis based active learning to improve the query efficiency in training the substitute model. Experiments on three convolutional neural network (CNN) classifiers and three EEG datasets demonstrated that our method can improve the attack success rate with the same number of queries, or, in other words, our method requires fewer queries to achieve a desired attack performance. To our knowledge, this is the first work that integrates active learning and adversarial attacks for EEG-based 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 "active learning for regression using greedy sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon adaptive code optimization for expedited deep neural network compilation")
  Member "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised pool based active learning for linear regression")
  Member "affect estimation in 3d space using multi task active learning for regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised pool based active learning for linear regression")
  Member "eegnet a compact convolutional neural network for eeg based brain computer interfaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")
  Member "black box adversarial attacks with limited queries and information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "audio adversarial examples targeted attacks on speech to text":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (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)