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: "real time and embedded deep learning on fpga for rf signal classification"
  Abstract: We designed and implemented a deep learning based RF signal classifier on the Field Programmable Gate Array (FPGA) of an embedded software-defined radio platform, DeepRadio, that classifies the signals received through the RF front end to different modulation types in real time and with low power. This classifier implementation successfully captures complex characteristics of wireless signals to serve critical applications in wireless security and communications systems such as identifying spoofing signals in signal authentication systems, detecting target emitters and jammers in electronic warfare (EW) applications, discriminating primary and secondary users in cognitive radio networks, interference hunting, and adaptive modulation. Empowered by low-power and low-latency embedded computing, the deep neural network runs directly on the FPGA fabric of DeepRadio, while maintaining classifier accuracy close to the software performance. We evaluated the performance when another SDR (USRP) transmits signals with different modulation types at different power levels and DeepRadio receives the signals and classifies them in real time on its FPGA. A smartphone with a mobile app is connected to DeepRadio to initiate the experiment and visualize the classification results. With real radio transmissions over the air, we show that the classifier implemented on DeepRadio achieves high accuracy with low latency (microsecond per sample) and low energy consumption (microJoule per sample), and this performance is not matched by other embedded platforms such as embedded graphics processing unit (GPU).

Connected through hyperedge HE-1 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions"):
  with members:
    - "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"
    - "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:
    - "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"
    - "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)