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: "deflecting 3d adversarial point clouds through outlier guided removal"
  Abstract: Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose simple random sampling (SRS) and statistical outlier removal (SOR) as defenses for 3D point cloud classification, where both methods remove points by estimating probability of points serving as adversarial points. Compared with ensemble adversarial training which is the state-of-the-art defending method, SOR has several advantages: better defense performance, randomization makes the network more robust to adversarial point clouds, no additional training or fine-tuning required, and few computations are needed by adding the points-removal layer. In particular, our experiments on ModelNet40 show that SOR is very effective as defense in practice. The strength of those defenses lies in their non-differentiable nature and inherent randomness, which makes it difficult for an adversary to circumvent the defenses. Our best defense eliminates 81.4% of strong white-box attacks by C&W and l2 loss based attack methods.

Connected through hyperedge HE-1 (induced by source paper "adversarial point perturbations on 3d objects"):
  with members:
    - "adversarial attack and defense on point sets"
    - "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"
    - "generating 3d adversarial point clouds"
    - "pointcloud saliency maps"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-2 (induced by source paper "extending adversarial attacks and defenses to deep 3d point cloud classifiers"):
  with members:
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "generating 3d adversarial point clouds"
    - "pointcloud saliency maps"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "interpreting deep visual representations via network dissection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

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)