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 "strategies to architect ai safety defense to guard ai from adversaries"):
  Abstract: The impact of designing for security of AI is critical for humanity in the AI era. With humans increasingly becoming dependent upon AI, there is a need for neural networks that work reliably, inspite of Adversarial attacks. The vision for Safe and secure AI for popular use is achievable. To achieve safety of AI, this paper explores strategies and a novel deep learning architecture. To guard AI from adversaries, paper explores combination of 3 strategies: #R##N#1. Introduce randomness at inference time to hide the representation learning from adversaries. #R##N#2. Detect presence of adversaries by analyzing the sequence of inferences. #R##N#3. Exploit visual similarity. #R##N#To realize these strategies, this paper designs a novel architecture, Dynamic Neural Defense, DND. This defense has 3 deep learning architectural features: #R##N#1. By hiding the way a neural network learns from exploratory attacks using a random computation graph, DND evades attack. #R##N#2. By analyzing input sequence to cloud AI inference engine with LSTM, DND detects attack sequence. #R##N#3. By inferring with visual similar inputs generated by VAE, any AI defended by DND approach does not succumb to hackers. #R##N#Thus, a roadmap to develop reliable, safe and secure AI is presented.

Includes members:
  Member "darts deceiving autonomous cars with toxic signs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "applying tensor decomposition to image for robustness against adversarial attack")
  Member "defensive distillation is not robust to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial machine learning at scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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.AI(Artificial Intelligence)