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: "spectrum data poisoning with adversarial deep learning"
  Abstract: Machine learning has been widely applied in wireless communications. However, the security aspects of machine learning in wireless applications have not been well understood yet. We consider the case that a cognitive transmitter senses the spectrum and transmits on idle channels determined by a machine learning algorithm. We present an adversarial machine learning approach to launch a spectrum data poisoning attack by inferring the transmitter's behavior and attempting to falsify the spectrum sensing data over the air. For that purpose, the adversary transmits for a short period of time when the channel is idle to manipulate the input for the decision mechanism of the transmitter. The cognitive engine at the transmitter is a deep neural network model that predicts idle channels with minimum sensing error for data transmissions. The transmitter collects spectrum sensing data and uses it as the input to its machine learning algorithm. In the meantime, the adversary builds a cognitive engine using another deep neural network model to predict when the transmitter will have a successful transmission based on its spectrum sensing data. The adversary then performs the over-the-air spectrum data poisoning attack, which aims to change the channel occupancy status from idle to busy when the transmitter is sensing, so that the transmitter is fooled into making incorrect transmit decisions. This attack is more energy efficient and harder to detect compared to jamming of data transmissions. We show that this attack is very effective and reduces the throughput of the transmitter substantially.

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"
    - "when attackers meet ai learning empowered attacks in cooperative spectrum sensing"
    - "generative adversarial networks for black box api attacks with limited training data"

Connected through hyperedge HE-2 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"
    - "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"

Connected through hyperedge HE-3 (induced by source paper "adversarial deep learning for over the air spectrum poisoning attacks"):
  with members:
    - "adversarial examples in rf deep learning detection of the attack and its physical robustness"
    - "communication without interception defense against deep learning based modulation detection"
    - "adversarial attacks and defences competition"
    - "active deep learning attacks under strict rate limitations for online api calls"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "generative adversarial learning for spectrum sensing"
    - "deep learning for launching and mitigating wireless jamming attacks"
    - "adversarial attacks on deep learning based radio signal classification"

Connected through hyperedge HE-4 (induced by source paper "real time and embedded deep learning on fpga for rf signal classification"):
  with members:
    - "deepradioid real time channel resilient optimization of deep learning based radio fingerprinting algorithms"
    - "qos and jamming aware wireless networking using deep reinforcement learning"
    - "iot network security from the perspective of adversarial deep learning"
    - "over the air deep learning based radio signal classification"
    - "a very brief introduction to machine learning with applications to communication systems"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "generative adversarial learning for spectrum sensing"
    - "a model driven deep learning network for mimo detection"

Connected through hyperedge HE-5 (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"
    - "when attackers meet ai learning empowered attacks in cooperative spectrum sensing"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deep learning for rf signal classification in unknown and dynamic spectrum environments"):
  with members:
    - "iot network security from the perspective of adversarial deep learning"
    - "over the air deep learning based radio signal classification"
    - "generative adversarial learning for spectrum sensing"
    - "deep learning for launching and mitigating wireless jamming attacks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "deep sensing cooperative spectrum sensing based on convolutional neural networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-7 (induced by source paper "iot network security from the perspective of adversarial deep learning"):
  with members:
    - "generative adversarial networks for black box api attacks with limited training data"
    - "active deep learning attacks under strict rate limitations for online api calls"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "generative adversarial learning for spectrum sensing"
    - "deep learning for launching and mitigating wireless jamming attacks"
    - "adversarial attacks on deep learning based radio signal classification"
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep sensing cooperative spectrum sensing based on convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "deep learning for launching and mitigating wireless jamming attacks"):
  with members:
    - "generative adversarial networks for black box api attacks with limited training data"
    - "active deep learning attacks under strict rate limitations for online api calls"
    - "generative adversarial learning for spectrum sensing"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "deep sensing cooperative spectrum sensing based on convolutional neural networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "learning from simulated and unsupervised images through adversarial training"
    - "convolutional radio modulation recognition networks"

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)