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 "deep learning for launching and mitigating wireless jamming attacks"):
  Abstract: An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented. A cognitive transmitter uses a pre-trained classifier to predict the current channel status based on recent sensing results and decides whether to transmit or not, whereas a jammer collects channel status and ACKs to build a deep learning classifier that reliably predicts the next successful transmissions and effectively jams them. This jamming approach is shown to reduce the transmitter's performance much more severely compared with random or sensing-based jamming. The deep learning classification scores are used by the jammer for power control subject to an average power constraint. Next, a generative adversarial network (GAN) is developed for the jammer to reduce the time to collect the training dataset by augmenting it with synthetic samples. As a defense scheme, the transmitter deliberately takes a small number of wrong actions in spectrum access (in form of a causative attack against the jammer) and therefore prevents the jammer from building a reliable classifier. The transmitter systematically selects when to take wrong actions and adapts the level of defense to mislead the jammer into making prediction errors and consequently increase its throughput.

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 "generative adversarial learning for spectrum sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "artificial neural networks based machine learning for wireless networks a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "deep sensing cooperative spectrum sensing based on convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial deep learning for over the air spectrum poisoning attacks")
  Member "power of deep learning for channel estimation and signal detection in ofdm systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "learning from simulated and unsupervised images through adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)