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 "adversarial point perturbations on 3d objects"):
  Abstract: The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks, like autonomous driving. We examine this problem from the perspective of the attacker, which is necessary in understanding how neural networks can be exploited, and thus defended. More specifically, we propose adversarial attacks based on solving different optimization problems, like minimizing the perceptibility of our generated adversarial examples, or maintaining a uniform density distribution of points across the adversarial object surfaces. Our four proposed algorithms for attacking 3D point cloud classification are all highly successful on existing neural networks, and we find that some of them are even effective against previously proposed point removal defenses.

Includes members:
  Member "adversarial attack and defense on point sets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on isometry robustness of deep 3d point cloud models under adversarial attacks")
  Member "extending adversarial attacks and defenses to deep 3d point cloud classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rearchitecting classification frameworks for increased robustness")
  Member "adversarial objects against lidar based autonomous driving systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "enhanced adversarial strategically timed attacks against deep reinforcement learning")
  Member "robustness of 3d deep learning in an adversarial setting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on isometry robustness of deep 3d point cloud models under adversarial attacks")
  Member "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a rotation invariant framework for deep point cloud analysis")
  Member "deflecting 3d adversarial point clouds through outlier guided removal":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "extending adversarial attacks and defenses to deep 3d point cloud classifiers")
  Member "generating 3d adversarial point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review deep learning on 3d point clouds")
  Member "pointcloud saliency maps":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on isometry robustness of deep 3d point cloud models under adversarial attacks")

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)