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: "adversarial examples in rf deep learning detection of the attack and its physical robustness"
  Abstract: While research on adversarial examples in machine learning for images has been prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation strategies are scarcely addressed in the published work, with only one recent publication in the RF domain [1]. RF adversarial examples (AdExs) can cause drastic, targeted misclassification results mostly in spectrum sensing/ survey applications (e.g. BPSK mistaken for 8-PSK) with minimal waveform perturbation. It is not clear if the RF AdExs maintain their effects in the physical world, i.e., when AdExs are delivered over-the-air (OTA). Our research on deep learning AdExs and proposed defense mechanisms are RF-centric, and incorporate physical world, OTA effects. We here present defense mechanisms based on statistical tests. One test to detect AdExs utilizes Peak-to- Average-Power-Ratio (PAPR) of the DL data points delivered OTA, while another statistical test uses the Softmax outputs of the DL classifier, which corresponds to the probabilities the classifier assigns to each of the trained classes. The former test leverages the RF nature of the data, and the latter is universally applicable to AdExs regardless of their origin. Both solutions are shown as viable mitigation methods to subvert adversarial attacks against communications and radar sensing systems.

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"
    - "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 "adversarial deep learning for over the air spectrum poisoning attacks"):
  with members:
    - "spectrum data poisoning with adversarial deep learning"
    - "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-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "adversarial machine learning attack on modulation classification"):
  with members:
    - "adversarial attacks on deep learning based radio signal classification"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "knowledge defined networking"
    - "why and how networks should run themselves"
    - "unsupervised representation learning of structured radio communication signals"
    - "convolutional radio modulation recognition networks"
    - "towards evaluating the robustness of neural networks"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "black box adversarial ml attack on modulation classification"):
  with members:
    - "adversarial attacks on deep learning based radio signal classification"
    - "why and how networks should run themselves"
    - "towards evaluating the robustness of neural 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.LG(Machine Learning)