human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "geometry aware generation of adversarial and cooperative point clouds"):
  Abstract: Recent studies show that machine learning models are vulnerable to adversarial examples. In 2D image domain, these examples are obtained by adding imperceptible noises to natural images. This paper studies adversarial generation of point clouds by learning to deform those approximating object surfaces of certain categories. As 2D manifolds embedded in the 3D Euclidean space, object surfaces enjoy the general properties of smoothness and fairness. We thus argue that in order to achieve imperceptible surface shape deformations, adversarial point clouds should have the same properties with similar degrees of smoothness/fairness to the benign ones, while being close to the benign ones as well when measured under certain distance metrics of point clouds. To this end, we propose a novel loss function to account for imperceptible, geometry-aware deformations of point clouds, and use the proposed loss in an adversarial objective to attack representative models of point set classifiers. Experiments show that our proposed method achieves stronger attacks than existing methods, without introduction of noticeable outliers and surface irregularities. In this work, we also investigate an opposite direction that learns to deform point clouds of object surfaces in the same geometry-aware, but cooperative manner. Cooperatively generated point clouds are more favored by machine learning models in terms of improved classification confidence or accuracy. We present experiments verifying that our proposed objective succeeds in learning cooperative shape deformations.

Includes members:
  Member "geonet deep geodesic networks for point cloud analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving semantic analysis on point clouds via auxiliary supervision of local geometric priors")
  Member "adversarial attack and defense on point sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on isometry robustness of deep 3d point cloud models under adversarial attacks")
  Member "adversarial sensor attack on lidar based perception in autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems")
  Member "extending adversarial attacks and defenses to deep 3d point cloud classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rearchitecting classification frameworks for increased robustness")
  Member "meshadv adversarial meshes for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing")
  Member "robustness of 3d deep learning in an adversarial setting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on isometry robustness of deep 3d point cloud models under adversarial attacks")
  Member "a skeleton bridged deep learning approach for generating meshes of complex topologies from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image")
  Member "adversarial point perturbations on 3d objects":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)