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: "meshadv adversarial meshes for visual recognition"
  Abstract: Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on perturbation added to image pixels, while such manipulation is not physically realistic. Some works have tried to overcome this limitation by attaching printable 2D patches or painting patterns onto surfaces, but can be potentially defended because 3D shape features are intact. In this paper, we propose meshAdv to generate "adversarial 3D meshes" from objects that have rich shape features but minimal textural variation. To manipulate the shape or texture of the objects, we make use of a differentiable renderer to compute accurate shading on the shape and propagate the gradient. Extensive experiments show that the generated 3D meshes are effective in attacking both classifiers and object detectors. We evaluate the attack under different viewpoints. In addition, we design a pipeline to perform black-box attack on a photorealistic renderer with unknown rendering parameters.

Connected through hyperedge HE-1 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing"):
  with members:
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "spatially transformed adversarial examples"
    - "cosface large margin cosine loss for deep face recognition"
    - "accelerated training for massive classification via dynamic class selection"
    - "large scale gan training for high fidelity natural image synthesis"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "towards stable and efficient training of verifiably robust neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "a provable defense for deep residual networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "verification of non linear specifications for neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "spatially transformed adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "smart skeletal motion action recognition attack"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "adversarial attacks on time series"
    - "adversarial attack on skeleton based human action recognition"
    - "enhancing adversarial example transferability with an intermediate level attack"
    - "spatio temporal manifold learning for human motions via long horizon modeling"
    - "adversarial attacks for optical flow based action recognition classifiers"
    - "spatially transformed adversarial examples"
    - "learning discriminative video representations using adversarial perturbations"

Connected through hyperedge HE-4 (induced by source paper "advpc transferable adversarial perturbations on 3d point clouds"):
  with members:
    - "point cloud oversegmentation with graph structured deep metric learning"
    - "cross domain transferability of adversarial perturbations"
    - "sada semantic adversarial diagnostic attacks for autonomous applications"
    - "generating 3d adversarial point clouds"
    - "pointcloud saliency maps"
    - "dynamic graph cnn for learning on point clouds"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "pu net point cloud upsampling network"

Connected through hyperedge HE-5 (induced by source paper "geometry aware generation of adversarial and cooperative point clouds"):
  with members:
    - "geonet deep geodesic networks for point cloud analysis"
    - "adversarial attack and defense on point sets"
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "robustness of 3d deep learning in an adversarial setting"
    - "a skeleton bridged deep learning approach for generating meshes of complex topologies from single rgb images"
    - "adversarial point perturbations on 3d objects"
    - "modeling local geometric structure of 3d point clouds using geo cnn"

Connected through hyperedge HE-6 (induced by source paper "adversarial objects against lidar based autonomous driving systems"):
  with members:
    - "spatially transformed adversarial examples"
    - "generating 3d adversarial point clouds"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "generating adversarial examples with adversarial networks"
    - "data poisoning attack against unsupervised node embedding methods"
    - "robust physical world attacks on deep learning models"
    - "synthesizing robust adversarial examples"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-7 (induced by source paper "sada semantic adversarial diagnostic attacks for autonomous applications"):
  with members:
    - "towards analyzing semantic robustness of deep neural networks"
    - "deep drone racing learning agile flight in dynamic environments"
    - "semantic adversarial examples"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "yolov3 an incremental improvement"
    - "generating 3d adversarial point clouds"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "sparse gaussian process temporal difference learning for marine robot navigation"

Connected through hyperedge HE-8 (induced by source paper "characterizing attacks on deep reinforcement learning"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "risk averse robust adversarial reinforcement learning"
    - "how you act tells a lot privacy leakage attack on deep reinforcement learning"
    - "spatially transformed adversarial examples"
    - "assessing generalization in deep reinforcement learning"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "exploring the space of black box attacks on deep neural networks"
    - "generating adversarial examples with adversarial networks"

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)