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 "ghostimage perception domain attacks against vision based object classification systems"):
  Abstract: In vision-based object classification systems, imaging sensors perceive the environment and then objects are detected and classified for decision-making purposes. Vulnerabilities in the perception domain enable an attacker to inject false data into the sensor which could lead to unsafe consequences. In this work, we focus on camera-based systems and propose GhostImage attacks, with the goal of either creating a fake perceived object or obfuscating the object's image that leads to wrong classification results. This is achieved by remotely projecting adversarial patterns into camera-perceived images, exploiting two common effects in optical imaging systems, namely lens flare/ghost effects, and auto-exposure control. To improve the robustness of the attack to channel perturbations, we generate optimal input patterns by integrating adversarial machine learning techniques with a trained end-to-end channel model. We realize GhostImage attacks with a projector, and conducted comprehensive experiments, using three different image datasets, in indoor and outdoor environments, and three different cameras. We demonstrate that GhostImage attacks are applicable to both autonomous driving and security surveillance scenarios. Experiment results show that, depending on the projector-camera distance, attack success rates can reach as high as 100%.

Includes members:
  Member "taxonomy and challenges of out of band signal injection attacks and defenses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trick or heat manipulating critical temperature based control systems using rectification attacks")
  Member "adversarial sensor attack on lidar based perception in autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rearchitecting classification frameworks for increased robustness")
  Member "making an invisibility cloak real world adversarial attacks on object detectors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attacking vision based perception in end to end autonomous driving models")
  Member "advhat real world adversarial attack on arcface face id system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "spatially transformed adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "stochastic activation pruning for robust adversarial defense":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "certified defenses against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "overfitting in adversarially robust deep learning")

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.CR(Cryptography and Security)