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: "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
  Abstract: It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived. However, existing certified robustnesses are limited to top-1 predictions. In many real-world applications, top-$k$ predictions are more relevant. In this work, we aim to derive certified robustness for top-$k$ predictions. In particular, our certified robustness is based on randomized smoothing, which turns any classifier to a new classifier via adding noise to an input example. We adopt randomized smoothing because it is scalable to large-scale neural networks and applicable to any classifier. We derive a tight robustness in $\ell_2$ norm for top-$k$ predictions when using randomized smoothing with Gaussian noise. We find that generalizing the certified robustness from top-1 to top-$k$ predictions faces significant technical challenges. We also empirically evaluate our method on CIFAR10 and ImageNet. For example, our method can obtain an ImageNet classifier with a certified top-5 accuracy of 62.8\% when the $\ell_2$-norms of the adversarial perturbations are less than 0.5 (=127/255). Our code is publicly available at: \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "on the security of randomized defenses against adversarial samples"):
  with members:
    - "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"
    - "certified adversarial robustness via randomized smoothing"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "on evaluating adversarial robustness"
    - "training verified learners with learned verifiers"
    - "breaking transferability of adversarial samples with randomness"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "are adversarial examples inevitable"

Connected through hyperedge HE-2 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness to graph perturbations"
    - "attacking graph based classification via manipulating the graph structure"
    - "adversarial attack on graph structured data"

Connected through hyperedge HE-3 (induced by source paper "black box certification with randomized smoothing a functional optimization based framework"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "certified adversarial robustness via randomized smoothing"
    - "adversarially robust generalization just requires more unlabeled data"
    - "improving neural language modeling via adversarial training"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial logit pairing"

Connected through hyperedge HE-4 (induced by source paper "random smoothing might be unable to certify ell_ infty robustness for high dimensional images"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "design and interpretation of universal adversarial patches in face detection"
    - "theoretically principled trade off between robustness and accuracy"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-5 (induced by source paper "on certifying robustness against backdoor attacks via randomized smoothing"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "strip a defence against trojan attacks on deep neural networks"
    - "defending neural backdoors via generative distribution modeling"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "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.LG(Machine Learning)