human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "towards probabilistic verification of machine unlearning"):
  with members:
    - "formalizing data deletion in the context of the right to be forgotten"
    - "machine unlearning linear filtration for logit based classifiers"
    - "bypassing backdoor detection algorithms in deep learning"
    - "hidden trigger backdoor attacks"
    - "making ai forget you data deletion in machine learning"
    - "certified data removal from machine learning models"
    - "auditing data provenance in text generation models"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Question: Please tell me which class the center node 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)