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: "wasserstein smoothing certified robustness against wasserstein adversarial attacks"
  Abstract: In the last couple of years, several adversarial attack methods based on different threat models have been proposed for the image classification problem. Most existing defenses consider additive threat models in which sample perturbations have bounded L_p norms. These defenses, however, can be vulnerable against adversarial attacks under non-additive threat models. An example of an attack method based on a non-additive threat model is the Wasserstein adversarial attack proposed by Wong et al. (2019), where the distance between an image and its adversarial example is determined by the Wasserstein metric ("earth-mover distance") between their normalized pixel intensities. Until now, there has been no certifiable defense against this type of attack. In this work, we propose the first defense with certified robustness against Wasserstein Adversarial attacks using randomized smoothing. We develop this certificate by considering the space of possible flows between images, and representing this space such that Wasserstein distance between images is upper-bounded by L_1 distance in this flow-space. We can then apply existing randomized smoothing certificates for the L_1 metric. In MNIST and CIFAR-10 datasets, we find that our proposed defense is also practically effective, demonstrating significantly improved accuracy under Wasserstein adversarial attack compared to unprotected models.

Connected through hyperedge HE-1 (induced by source paper "randomized smoothing of all shapes and sizes"):
  with members:
    - "curse of dimensionality on randomized smoothing for certifiable robustness"
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "robustness certificates for sparse adversarial attacks by randomized ablation"
    - "certified adversarial robustness via randomized smoothing"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "training verified learners with learned verifiers"

Connected through hyperedge HE-2 (induced by source paper "de randomized smoothing for certifiable defense against patch attacks"):
  with members:
    - "curse of dimensionality on randomized smoothing for certifiable robustness"
    - "randomized smoothing of all shapes and sizes"
    - "certified robustness to label flipping attacks via randomized smoothing"
    - "random smoothing might be unable to certify ell_ infty robustness for high dimensional images"
    - "certified defenses for adversarial patches"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "robustness certificates for sparse adversarial attacks by randomized ablation"
    - "certified adversarial robustness via randomized smoothing"

Connected through hyperedge HE-3 (induced by source paper "amora black box adversarial morphing attack"):
  with members:
    - "fakelocator robust localization of gan based face manipulations via semantic segmentation networks with bells and whistles"
    - "advfaces adversarial face synthesis"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "attacking optical flow"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "machine learning testing survey landscapes and horizons"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"

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)