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 "darts deceiving autonomous cars with toxic signs"):
  Abstract: Sign recognition is an integral part of autonomous cars. Any misclassification of traffic signs can potentially lead to a multitude of disastrous consequences, ranging from a life-threatening accident to a large-scale interruption of transportation services relying on autonomous cars. In this paper, we propose and examine realistic security attacks against sign recognition systems for Deceiving Autonomous caRs with Toxic Signs (we call the proposed attacks DARTS). #R##N#Leveraging the concept of adversarial examples, we modify innocuous signs/advertisements in the environment in such a way that they seem normal to human observers but are interpreted as the adversary's desired traffic sign by autonomous cars. Further, we pursue a fundamentally different perspective to attacking autonomous cars, motivated by the observation that the driver and vehicle-mounted camera see the environment from different angles (the camera commonly sees the road with a higher angle, e.g., from top of the car). We propose a novel attack against vehicular sign recognition systems: we create signs that change as they are viewed from different angles, and thus, can be interpreted differently by the driver and sign recognition. #R##N#We extensively evaluate the proposed attacks under various conditions: different distances, lighting conditions, and camera angles. We first examine our attacks virtually, i.e., we check if the digital images of toxic signs can deceive the sign recognition system. Further, we investigate the effectiveness of attacks in real-world settings: we print toxic signs, install them in the environment, capture videos using a vehicle-mounted camera, and process them using our sign recognition pipeline.

Includes members:
  Member "pinme tracking a smartphone user around the world":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mitigating location privacy attacks on mobile devices using dynamic app sandboxing")
  Member "shapeshifter robust physical adversarial attack on faster r cnn object detector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "exploring the space of black box attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "at gan an adversarial generator model for non constrained adversarial examples")
  Member "feature squeezing detecting adversarial examples in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "adversarial attacks on neural network policies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "magnet a two pronged defense against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "on the robustness of semantic segmentation models to adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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)