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: "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
  Abstract: 3D object classification and segmentation using deep neural networks has been extremely successful. As the problem of identifying 3D objects has many safety-critical applications, the neural networks have to be robust against adversarial changes to the input data set. There is a growing body of research on generating human-imperceptible adversarial attacks and defenses against them in the 2D image classification domain. However, 3D objects have various differences with 2D images, and this specific domain has not been rigorously studied so far. #R##N#We present a preliminary evaluation of adversarial attacks on deep 3D point cloud classifiers, namely PointNet and PointNet++, by evaluating both white-box and black-box adversarial attacks that were proposed for 2D images and extending those attacks to reduce the perceptibility of the perturbations in 3D space. We also show the high effectiveness of simple defenses against those attacks by proposing new defenses that exploit the unique structure of 3D point clouds. Finally, we attempt to explain the effectiveness of the defenses through the intrinsic structures of both the point clouds and the neural network architectures. Overall, we find that networks that process 3D point cloud data are weak to adversarial attacks, but they are also more easily defensible compared to 2D image classifiers. Our investigation will provide the groundwork for future studies on improving the robustness of deep neural networks that handle 3D data.

Connected through hyperedge HE-1 (induced by source paper "rearchitecting classification frameworks for increased robustness"):
  with members:
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "certified adversarial robustness via randomized smoothing"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "humans can decipher adversarial images"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "on evaluating adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"

Connected through hyperedge HE-2 (induced by source paper "samplenet differentiable point cloud sampling"):
  with members:
    - "adaptive hierarchical down sampling for point cloud classification"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "unsupervised detection of distinctive regions on 3d shapes"
    - "logan unpaired shape transform in latent overcomplete space"
    - "kpconv flexible and deformable convolution for point clouds"
    - "pcrnet point cloud registration network using pointnet encoding"
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "unpaired point cloud completion on real scans using adversarial training"

Connected through hyperedge HE-3 (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"
    - "meshadv adversarial meshes for visual recognition"
    - "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-4 (induced by source paper "adversarial point perturbations on 3d objects"):
  with members:
    - "adversarial attack and defense on point sets"
    - "adversarial objects against lidar based autonomous driving systems"
    - "robustness of 3d deep learning in an adversarial setting"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "deflecting 3d adversarial point clouds through outlier guided removal"
    - "generating 3d adversarial point clouds"
    - "pointcloud saliency maps"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-5 (induced by source paper "visualizing point cloud classifiers by curvature smoothing"):
  with members:
    - "visualizing deep networks by optimizing with integrated gradients"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "generating 3d adversarial point clouds"
    - "pointcloud saliency maps"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-6 (induced by source paper "s4g amodal single view single shot se 3 grasp detection in cluttered scenes"):
  with members:
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning 6 dof grasping and pick place using attention focus"
    - "grasp2vec learning object representations from self supervised grasping"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "on the continuity of rotation representations in neural networks"
    - "grasp pose detection in point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-7 (induced by source paper "enhancing ml robustness using physical world constraints"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "on evaluating adversarial robustness"
    - "designing adversarially resilient classifiers using resilient feature engineering"
    - "yolov3 an incremental improvement"
    - "generating 3d adversarial point clouds"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "dup net denoiser and upsampler network for 3d adversarial point clouds defense"):
  with members:
    - "adversarial attack and defense on point sets"
    - "one pixel attack for fooling deep neural networks"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "realistic adversarial examples in 3d meshes"
    - "generating 3d adversarial point clouds"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "deep sets"

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.CV(Computer Vision and Pattern Recognition)