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: "on the effectiveness of defensive distillation"
  Abstract: We report experimental results indicating that defensive distillation successfully mitigates adversarial samples crafted using the fast gradient sign method, in addition to those crafted using the Jacobian-based iterative attack on which the defense mechanism was originally evaluated.

Connected through hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "sparse and imperceivable adversarial attacks"
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "hopskipjumpattack a query efficient decision based attack"
    - "towards adversarially robust object detection"
    - "defense against adversarial images using web scale nearest neighbor search"

Connected through hyperedge HE-2 (induced by source paper "learn2perturb an end to end feature perturbation learning to improve adversarial robustness"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "robust neural networks using randomized adversarial training"
    - "theoretical evidence for adversarial robustness through randomization the case of the exponential family"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "feature denoising for improving adversarial robustness"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-3 (induced by source paper "adversarial robustness via label smoothing"):
  with members:
    - "adversarial examples opportunities and challenges"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "darts deceiving autonomous cars with toxic signs"
    - "regularizing neural networks by penalizing confident output distributions"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "towards evaluating the robustness of neural networks"
    - "robustness of classifiers from adversarial to random noise"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-4 (induced by source paper "the human visual system and adversarial ai"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "adversarial logit pairing"
    - "query efficient hard label black box attack an optimization based approach"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-5 (induced by source paper "certifiably robust interpretation in deep learning"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "towards robust locally linear deep networks"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "generalizing multistain immunohistochemistry tissue segmentation using one shot color deconvolution deep neural networks"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "certified adversarial robustness with additive noise"
    - "sanity checks for saliency maps"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "adversarial robustness via adversarial label smoothing"):
  with members:
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "adversarial examples opportunities and challenges"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "darts deceiving autonomous cars with toxic signs"
    - "regularizing neural networks by penalizing confident output distributions"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-7 (induced by source paper "sok security and privacy in machine learning"):
  with members:
    - "poisoning behavioral malware clustering"
    - "is feature selection secure against training data poisoning"
    - "adversarial attacks on neural network policies"
    - "certified defenses for data poisoning attacks"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "algorithmic decision making and the cost of fairness"
    - "adversarial perturbations against deep neural networks for malware classification"

Connected through hyperedge HE-8 (induced by source paper "peernets exploiting peer wisdom against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deep dictionary learning a parametric network approach"
    - "adversarial attacks and defences competition"
    - "dynamic graph cnn for learning on point clouds"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "non local neural networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"

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)