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 "adversarial deep learning for over the air spectrum poisoning attacks"):
  Abstract: An adversarial deep learning approach is presented to launch over-the-air spectrum poisoning attacks. A transmitter applies deep learning on its spectrum sensing results to predict idle time slots for data transmission. In the meantime, an adversary learns the transmitter's behavior (exploratory attack) by building another deep neural network to predict when transmissions will succeed. The adversary falsifies (poisons) the transmitter's spectrum sensing data over the air by transmitting during the short spectrum sensing period of the transmitter. Depending on whether the transmitter uses the sensing results as test data to make transmit decisions or as training data to retrain its deep neural network, either it is fooled into making incorrect decisions (evasion attack), or the transmitter's algorithm is retrained incorrectly for future decisions (causative attack). Both attacks are energy efficient and hard to detect (stealth) compared to jamming the long data transmission period, and substantially reduce the throughput. A dynamic defense is designed for the transmitter that deliberately makes a small number of incorrect transmissions (selected by the confidence score on channel classification) to manipulate the adversary's training data. This defense effectively fools the adversary (if any) and helps the transmitter sustain its throughput with or without an adversary present.

Includes members:
  Member "adversarial examples in rf deep learning detection of the attack and its physical robustness":
    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 "communication without interception defense against deep learning based modulation detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "adversarial attacks and defences competition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks")
  Member "active deep learning attacks under strict rate limitations for online api calls":
    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 signal authentication and security in massive internet of things systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trojan attacks on wireless signal classification with adversarial machine learning")
  Member "generative adversarial learning for spectrum sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")

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)