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: "generating 3d adversarial point clouds"
  Abstract: Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions. While adversarial examples for 2D images and CNNs have been extensively studied, less attention has been paid to 3D data such as point clouds. Given many safety-critical 3D applications such as autonomous driving, it is important to study how adversarial point clouds could affect current deep 3D models. In this work, we propose several novel algorithms to craft adversarial point clouds against PointNet, a widely used deep neural network for point cloud processing. Our algorithms work in two ways: adversarial point perturbation and adversarial point generation. For point perturbation, we shift existing points negligibly. For point generation, we generate either a set of independent and scattered points or a small number (1-3) of point clusters with meaningful shapes such as balls and airplanes which could be hidden in the human psyche. In addition, we formulate six perturbation measurement metrics tailored to the attacks in point clouds and conduct extensive experiments to evaluate the proposed algorithms on the ModelNet40 3D shape classification dataset. Overall, our attack algorithms achieve a success rate higher than 99% for all targeted attacks

Connected through hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "shape2motion joint analysis of motion parts and attributes from 3d shapes"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "pixor real time 3d object detection from point clouds"
    - "densepoint learning densely contextual representation for efficient point cloud processing"

Connected through hyperedge HE-2 (induced by source paper "on isometry robustness of deep 3d point cloud models under adversarial attacks"):
  with members:
    - "adversarial attack and defense on point sets"
    - "effective rotation invariant point cnn with spherical harmonics kernels"
    - "srinet learning strictly rotation invariant representations for point cloud classification and segmentation"
    - "robustness of 3d deep learning in an adversarial setting"
    - "theoretically principled trade off between robustness and accuracy"
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "relation shape convolutional neural network for point cloud analysis"
    - "modeling local geometric structure of 3d point clouds using geo cnn"

Connected through hyperedge HE-3 (induced by source paper "skd unsupervised keypoint detection for point clouds using saliency estimation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "point voxel cnn for efficient 3d deep learning"
    - "elf embedded localisation of features in pre trained cnn"
    - "deep closest point learning representations for point cloud registration"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-4 (induced by source paper "defense pointnet protecting pointnet against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "cornernet detecting objects as paired keypoints"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "a3t adversarially augmented adversarial training"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-5 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "rearchitecting classification frameworks for increased robustness"):
  with members:
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "samplenet differentiable point cloud sampling"):
  with members:
    - "adaptive hierarchical down sampling for point cloud classification"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "smoothed inference for adversarially trained models"):
  with members:
    - "robust unsupervised neural machine translation with adversarial training"
    - "maxup a simple way to improve generalization of neural network training"
    - "randomized smoothing of all shapes and sizes"
    - "colored noise injection for training adversarially robust neural networks"
    - "enforcing linearity in dnn succours robustness and adversarial image generation"
    - "training robust deep neural networks via adversarial noise propagation"
    - "adversarial policies attacking deep reinforcement learning"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"

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)