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 objects against lidar based autonomous driving systems"
  Abstract: Deep neural networks (DNNs) are found to be vulnerable against adversarial examples, which are carefully crafted inputs with a small magnitude of perturbation aiming to induce arbitrarily incorrect predictions. Recent studies show that adversarial examples can pose a threat to real-world security-critical applications: a "physical adversarial Stop Sign" can be synthesized such that the autonomous driving cars will misrecognize it as others (e.g., a speed limit sign). However, these image-space adversarial examples cannot easily alter 3D scans of widely equipped LiDAR or radar on autonomous vehicles. In this paper, we reveal the potential vulnerabilities of LiDAR-based autonomous driving detection systems, by proposing an optimization based approach LiDAR-Adv to generate adversarial objects that can evade the LiDAR-based detection system under various conditions. We first show the vulnerabilities using a blackbox evolution-based algorithm, and then explore how much a strong adversary can do, using our gradient-based approach LiDAR-Adv. We test the generated adversarial objects on the Baidu Apollo autonomous driving platform and show that such physical systems are indeed vulnerable to the proposed attacks. We also 3D-print our adversarial objects and perform physical experiments to illustrate that such vulnerability exists in the real world. Please find more visualizations and results on the anonymous website: this https URL.

Connected through hyperedge HE-1 (induced by source paper "enhanced adversarial strategically timed attacks against deep reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "on the use of deep autoencoders for efficient embedded reinforcement learning"
    - "when causal intervention meets adversarial examples and image masking for deep neural networks"
    - "dopamine a research framework for deep reinforcement learning"
    - "adversarial attacks and defences competition"
    - "unity a general platform for intelligent agents"
    - "baidu apollo em motion planner"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-2 (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"
    - "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"
    - "pointcloud saliency maps"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-3 (induced by source paper "adversarial t shirt evading person detectors in a physical world"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "beyond adversarial training min max optimization in adversarial attack and defense"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "structured adversarial attack towards general implementation and better interpretability"

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)