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 sensor attack on lidar based perception in autonomous driving"
  Abstract: In Autonomous Vehicles (AVs), one fundamental pillar is perception, which leverages sensors like cameras and LiDARs (Light Detection and Ranging) to understand the driving environment. Due to its direct impact on road safety, multiple prior efforts have been made to study its the security of perception systems. In contrast to prior work that concentrates on camera-based perception, in this work we perform the first security study of LiDAR-based perception in AV settings, which is highly important but unexplored. We consider LiDAR spoofing attacks as the threat model and set the attack goal as spoofing obstacles close to the front of a victim AV. We find that blindly applying LiDAR spoofing is insufficient to achieve this goal due to the machine learning-based object detection process. Thus, we then explore the possibility of strategically controlling the spoofed attack to fool the machine learning model. We formulate this task as an optimization problem and design modeling methods for the input perturbation function and the objective function. We also identify the inherent limitations of directly solving the problem using optimization and design an algorithm that combines optimization and global sampling, which improves the attack success rates to around 75%. As a case study to understand the attack impact at the AV driving decision level, we construct and evaluate two attack scenarios that may damage road safety and mobility. We also discuss defense directions at the AV system, sensor, and machine learning model levels.

Connected through hyperedge HE-1 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems"):
  with members:
    - "taxonomy and challenges of out of band signal injection attacks and defenses"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "advhat real world adversarial attack on arcface face id system"
    - "spatially transformed adversarial examples"
    - "stochastic activation pruning for robust adversarial defense"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "rearchitecting classification frameworks for increased robustness"):
  with members:
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "humans can decipher adversarial images"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "on evaluating adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"

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 attack and defense on point sets"
    - "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 "robust gabor networks"):
  with members:
    - "kervolutional neural networks"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "the singular values of convolutional layers"
    - "robustness via curvature regularization and vice versa"
    - "structured adversarial attack towards general implementation and better interpretability"
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "deep watershed transform for instance segmentation"
    - "wide residual networks"

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.CR(Cryptography and Security)