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: "functional adversarial attacks"
  Abstract: We propose functional adversarial attacks, a novel class of threat models for crafting adversarial examples to fool machine learning models. Unlike a standard $\ell_p$-ball threat model, a functional adversarial threat model allows only a single function to be used to perturb input features to produce an adversarial example. For example, a functional adversarial attack applied on colors of an image can change all red pixels simultaneously to light red. Such global uniform changes in images can be less perceptible than perturbing pixels of the image individually. For simplicity, we refer to functional adversarial attacks on image colors as ReColorAdv, which is the main focus of our experiments. We show that functional threat models can be combined with existing additive ($\ell_p$) threat models to generate stronger threat models that allow both small, individual perturbations and large, uniform changes to an input. Moreover, we prove that such combinations encompass perturbations that would not be allowed in either constituent threat model. In practice, ReColorAdv can significantly reduce the accuracy of a ResNet-32 trained on CIFAR-10. Furthermore, to the best of our knowledge, combining ReColorAdv with other attacks leads to the strongest existing attack even after adversarial training.

Connected through hyperedge HE-1 (induced by source paper "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "generating realistic unrestricted adversarial inputs using dual objective gan training"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-2 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "colorfool semantic adversarial colorization"
    - "a general framework for adversarial examples with objectives"
    - "panoptic feature pyramid networks"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "playing it safe adversarial robustness with an abstain option"):
  with members:
    - "controlling neural level sets"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "spatially transformed adversarial examples"
    - "are adversarial examples inevitable"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"

Connected through hyperedge HE-4 (induced by source paper "wasserstein smoothing certified robustness against wasserstein adversarial attacks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "the attack generator a systematic approach towards constructing adversarial attacks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "certified adversarial robustness with additive noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

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)