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 "ensemble noise simulation to handle uncertainty about gradient based adversarial attacks"):
  Abstract: Gradient-based adversarial attacks on neural networks can be crafted in a variety of ways by varying either how the attack algorithm relies on the gradient, the network architecture used for crafting the attack, or both. Most recent work has focused on defending classifiers in a case where there is no uncertainty about the attacker's behavior (i.e., the attacker is expected to generate a specific attack using a specific network architecture). However, if the attacker is not guaranteed to behave in a certain way, the literature lacks methods in devising a strategic defense. We fill this gap by simulating the attacker's noisy perturbation using a variety of attack algorithms based on gradients of various classifiers. We perform our analysis using a pre-processing Denoising Autoencoder (DAE) defense that is trained with the simulated noise. We demonstrate significant improvements in post-attack accuracy, using our proposed ensemble-trained defense, compared to a situation where no effort is made to handle uncertainty.

Includes members:
  Member "transferable adversarial robustness using adversarially trained autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can attention masks improve adversarial robustness")
  Member "a computationally efficient method for defending adversarial deep learning attacks":
    also connected through other hyperedges:
      (none)
  Member "noise flooding for detecting audio adversarial examples against automatic speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "imperio robust over the air adversarial examples for automatic speech recognition systems")
  Member "training multi task adversarial network for extracting noise robust speaker embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "delving into voxceleb environment invariant speaker recognition")
  Member "breaking transferability of adversarial samples with randomness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the security of randomized defenses against adversarial samples")
  Member "boosting noise robustness of acoustic model via deep adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ufans u shaped fully parallel acoustic neural structure for statistical parametric speech synthesis with 20x faster")
  Member "combatting adversarial attacks through denoising and dimensionality reduction a cascaded autoencoder approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "denoising and verification cross layer ensemble against black box adversarial attacks")
  Member "enhancing robustness of machine learning systems via data transformations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")

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)