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: "adversarial attack and defense on point sets"
  Abstract: Emergence of the utility of 3D point cloud data in critical vision tasks (e.g., ADAS) urges researchers to pay more attention to the robustness of 3D representations and deep networks. To this end, we develop an attack and defense scheme, dedicated to 3D point cloud data, for preventing 3D point clouds from manipulated as well as pursuing noise-tolerable 3D representation. A set of novel 3D point cloud attack operations are proposed via pointwise gradient perturbation and adversarial point attachment / detachment. We then develop a flexible perturbation-measurement scheme for 3D point cloud data to detect potential attack data or noisy sensing data. Extensive experimental results on common point cloud benchmarks demonstrate the validity of the proposed 3D attack and defense framework.

Connected through hyperedge HE-1 (induced by source paper "on isometry robustness of deep 3d point cloud models under adversarial attacks"):
  with members:
    - "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"
    - "adversarial spheres"

Connected through hyperedge HE-2 (induced by source paper "modeling point clouds with self attention and gumbel subset sampling"):
  with members:
    - "image transformer"
    - "attention based deep multiple instance learning"
    - "splatnet sparse lattice networks for point cloud processing"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

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 sensor attack on lidar based perception in autonomous driving"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "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:
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "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 "dup net denoiser and upsampler network for 3d adversarial point clouds defense"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "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)