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 "iot network security from the perspective of adversarial deep learning"):
  Abstract: Machine learning finds rich applications in Internet of Things (IoT) networks such as information retrieval, traffic management, spectrum sensing, and signal authentication. While there is a surge of interest to understand the security issues of machine learning, their implications have not been understood yet for wireless applications such as those in IoT systems that are susceptible to various attacks due the open and broadcast nature of wireless communications. To support IoT systems with heterogeneous devices of different priorities, we present new techniques built upon adversarial machine learning and apply them to three types of over-the-air (OTA) wireless attacks, namely jamming, spectrum poisoning, and priority violation attacks. By observing the spectrum, the adversary starts with an exploratory attack to infer the channel access algorithm of an IoT transmitter by building a deep neural network classifier that predicts the transmission outcomes. Based on these prediction results, the wireless attack continues to either jam data transmissions or manipulate sensing results over the air (by transmitting during the sensing phase) to fool the transmitter into making wrong transmit decisions in the test phase (corresponding to an evasion attack). When the IoT transmitter collects sensing results as training data to retrain its channel access algorithm, the adversary launches a causative attack to manipulate the input data to the transmitter over the air. We show that these attacks with different levels of energy consumption and stealthiness lead to significant loss in throughput and success ratio in wireless communications for IoT systems. Then we introduce a defense mechanism that systematically increases the uncertainty of the adversary at the inference stage and improves the performance. Results provide new insights on how to attack and defend IoT networks using deep learning.

Includes members:
  Member "generative adversarial networks for black box api attacks with limited training data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "spectrum data poisoning with adversarial deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "active deep learning attacks under strict rate limitations for online api calls":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "deep learning for signal authentication and security in massive internet of things systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial deep learning for over the air spectrum poisoning attacks")
  Member "generative adversarial learning for spectrum sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "deep learning for launching and mitigating wireless jamming attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "adversarial attacks on deep learning based radio signal classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "deep learning for iot big data and streaming analytics a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")

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.NI(Networking and Internet Architecture)