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 "nnoculation broad spectrum and targeted treatment of backdoored dnns"):
  Abstract: This paper proposes a novel two-stage defense (NNoculation) against backdoored neural networks (BadNets) that, unlike existing defenses, makes minimal assumptions on the shape, size and location of backdoor triggers and BadNet's functioning. In the pre-deployment stage, NNoculation retrains the network using "broad-spectrum" random perturbations of inputs drawn from a clean validation set to partially reduce the adversarial impact of a backdoor. In the post-deployment stage, NNoculation detects and quarantines backdoored test inputs by recording disagreements between the original and pre-deployment patched networks. A CycleGAN is then trained to learn transformations between clean validation inputs and quarantined inputs; i.e., it learns to add triggers to clean validation images. This transformed set of backdoored validation images along with their correct labels is used to further retrain the BadNet, yielding our final defense. NNoculation outperforms state-of-the-art defenses NeuralCleanse and Artificial Brain Simulation (ABS) that we show are ineffective when their restrictive assumptions are circumvented by the attacker.

Includes members:
  Member "are adversarial perturbations a showstopper for ml based cad a case study on cnn based lithographic hotspot detection":
    also connected through other hyperedges:
      (none)
  Member "strip a defence against trojan attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "defending neural backdoors via generative distribution modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on certifying robustness against backdoor attacks via randomized smoothing")
  Member "wild patterns ten years after the rise of adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "fine pruning defending against backdooring attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "generative adversarial networks an overview":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "spectral signatures in backdoor attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "fastmri an open dataset and benchmarks for accelerated mri":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")

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.CR(Cryptography and Security)