human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "when attackers meet ai learning empowered attacks in cooperative spectrum sensing"
  Abstract: Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified sensing data. However, existing studies usually make network or attack assumptions biased towards the defense (e.g., assuming the prior knowledge of attacks is known). In practice, attackers can adopt any arbitrary behavior and avoid any pre-assumed pattern or assumption used by defense strategies. In this paper, we revisit this traditional security problem and propose a novel learning-empowered framework named Learn-Evaluate-Beat (LEB) to mislead the fusion center. Based on the black-box nature of the fusion center in cooperative spectrum sensing process, our new perspective is to make the adversarial use of machine learning to construct a surrogate model of the fusion center's decision model. Then, we propose a generic algorithm to create malicious sensing data. Our real-world experiments show that the LEB attack is very effective to beat a wide range of existing defense strategies with an up to 82% of success ratio. Given the gap between the new LEB attack and existing defenses, we introduce a non-invasive and parallel method named as influence-limiting policy sided with existing defenses to defend against the LEB-based or other similar attacks, which demonstrates a strong performance in terms of overall disruption ratio reduction by up to 80% of the LEB attacks.

Connected through hyperedge HE-1 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions"):
  with members:
    - "real time and embedded deep learning on fpga for rf signal classification"
    - "adversarial examples in rf deep learning detection of the attack and its physical robustness"
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "adversarial deep learning for over the air spectrum poisoning attacks"
    - "trojan attacks on wireless signal classification with adversarial machine learning"
    - "deepwifi cognitive wifi with deep learning"
    - "generative adversarial networks for black box api attacks with limited training data"
    - "qos and jamming aware wireless networking using deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "trojan attacks on wireless signal classification with adversarial machine learning"):
  with members:
    - "real time and embedded deep learning on fpga for rf signal classification"
    - "adversarial examples in rf deep learning detection of the attack and its physical robustness"
    - "spectrum data poisoning with adversarial deep learning"
    - "iot network security from the perspective of adversarial deep learning"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "adversarial attacks and defences competition"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "generative adversarial learning for spectrum sensing"

Question: Please tell me which class the center node 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)