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 audio perturbations"):
  Abstract: We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenarios. We propose two methods for finding such perturbations. The first method is based on an iterative, greedy approach that is well-known in computer vision: it aggregates small perturbations to the input so as to push it to the decision boundary. The second method, which is the main contribution of this work, is a novel penalty formulation, which finds targeted and untargeted universal adversarial perturbations. Differently from the greedy approach, the penalty method minimizes an appropriate objective function on a batch of samples. Therefore, it produces more successful attacks when the number of training samples is limited. Moreover, we provide a proof that the proposed penalty method theoretically converges to a solution that corresponds to universal adversarial perturbations. We also demonstrate that it is possible to provide successful attacks using the penalty method when only one sample from the target dataset is available for the attacker. Experimental results on attacking five 1D CNN architectures have shown attack success rates higher than 85.4% and 83.1% for targeted and untargeted attacks, respectively using the proposed penalty method.

Includes members:
  Member "robust audio adversarial example for a physical attack":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "end to end environmental sound classification using a 1d convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network")
  Member "universal adversarial perturbations for speech recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial deepfakes evaluating vulnerability of deepfake detectors to adversarial examples")
  Member "sirenattack generating adversarial audio for end to end acoustic systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "structure invariant testing for machine translation")
  Member "adversarial initialization when your network performs the way i want":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "salvaging federated learning by local adaptation")
  Member "unsupervised cross modal alignment of speech and text embedding spaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "representation learning for electronic health records")
  Member "wild patterns ten years after the rise of adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "unsupervised text style transfer using language models as discriminators":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blank language models")

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)