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: "pointcloud saliency maps"
  Abstract: 3D point-cloud recognition with PointNet and its variants has received remarkable progress. A missing ingredient, however, is the ability to automatically evaluate point-wise importance w.r.t.\! classification performance, which is usually reflected by a saliency map. A saliency map is an important tool as it allows one to perform further processes on point-cloud data. In this paper, we propose a novel way of characterizing critical points and segments to build point-cloud saliency maps. Our method assigns each point a score reflecting its contribution to the model-recognition loss. The saliency map explicitly explains which points are the key for model recognition. Furthermore, aggregations of highly-scored points indicate important segments/subsets in a point-cloud. Our motivation for constructing a saliency map is by point dropping, which is a non-differentiable operator. To overcome this issue, we approximate point-dropping with a differentiable procedure of shifting points towards the cloud centroid. Consequently, each saliency score can be efficiently measured by the corresponding gradient of the loss w.r.t the point under the spherical coordinates. Extensive evaluations on several state-of-the-art point-cloud recognition models, including PointNet, PointNet++ and DGCNN, demonstrate the veracity and generality of our proposed saliency map. Code for experiments is released on \url{this https URL}.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization"):
  with members:
    - "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "atloc attention guided camera localization"
    - "deep hough voting for 3d object detection in point clouds"
    - "deeppco end to end point cloud odometry through deep parallel neural network"
    - "deep auxiliary learning for visual localization and odometry"
    - "flownet3d learning scene flow in 3d point clouds"

Connected through hyperedge HE-4 (induced by source paper "utility analysis of network architectures for 3d point cloud processing"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "similarity of neural network representations revisited"
    - "quantifying layerwise information discarding of neural networks"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "3d point capsule networks"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "multiresolution tree networks for 3d point cloud processing"

Connected through hyperedge HE-5 (induced by source paper "advpc transferable adversarial perturbations on 3d point clouds"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "point cloud oversegmentation with graph structured deep metric learning"
    - "cross domain transferability of adversarial perturbations"
    - "sada semantic adversarial diagnostic attacks for autonomous applications"
    - "generating 3d adversarial point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "pu net point cloud upsampling network"

Connected through hyperedge HE-6 (induced by source paper "geometry aware generation of adversarial and cooperative point clouds"):
  with members:
    - "geonet deep geodesic networks for point cloud analysis"
    - "adversarial attack and defense on point sets"
    - "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"

Connected through hyperedge HE-7 (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"
    - "deflecting 3d adversarial point clouds through outlier guided removal"
    - "generating 3d adversarial point clouds"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-8 (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"
    - "deflecting 3d adversarial point clouds through outlier guided removal"
    - "generating 3d adversarial point clouds"
    - "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)