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: "deep learning for signal authentication and security in massive internet of things systems"
  Abstract: Secure signal authentication is arguably one of the most challenging problems in the Internet of Things (IoT) environment, due to the large-scale nature of the system and its susceptibility to man-in-the-middle and eavesdropping attacks. In this paper, a novel deep learning method is proposed for dynamic authentication of IoT signals to detect cyber attacks. The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices (IoTDs) to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the cloud, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Moreover, in massive IoT scenarios, since the cloud cannot authenticate all the IoTDs simultaneously due to computational limitations, a game-theoretic framework is proposed to improve the cloud's decision making process by predicting vulnerable IoTDs. The mixed-strategy Nash equilibrium (MSNE) for this game is derived and the uniqueness of the expected utility at the equilibrium is proven. In the massive IoT system, due to a large set of available actions for the cloud, it is shown that analytically deriving the MSNE is challenging and, thus, a learning algorithm proposed that converges to the MSNE. Moreover, in order to cope with the incomplete information case in which the cloud cannot access the state of the unauthenticated IoTDs, a deep reinforcement learning algorithm is proposed to dynamically predict the state of unauthenticated IoTDs and allow the cloud to decide on which IoTDs to authenticate. Simulation results show that, with an attack detection delay of under 1 second the messages can be transmitted from IoT devices with an almost 100% reliability.

Connected through hyperedge HE-1 (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"
    - "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"
    - "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-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"
    - "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"
    - "generative adversarial learning for spectrum sensing"

Connected through hyperedge HE-3 (induced by source paper "contextual bandit learning for machine type communications in the null space of multi antenna systems"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"
    - "a directed information learning framework for event driven m2m traffic prediction"
    - "fast uplink grant for machine type communications challenges and opportunities"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "random access schemes in wireless systems with correlated user activity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "distributed learning for low latency machine type communication in a massive internet of things"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial networks for distributed intrusion detection in the internet of things"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "efficient gan based anomaly detection"
    - "md gan multi discriminator generative adversarial networks for distributed datasets"
    - "mdgan boosting anomaly detection using multi discriminator generative adversarial networks"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using generative adversarial nets"

Connected through hyperedge HE-5 (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"
    - "spectrum data poisoning with adversarial deep learning"
    - "active deep learning attacks under strict rate limitations for online api calls"
    - "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-6 (induced by source paper "controllable planning responsibility and information in automatic driving technology"):
  with members:
    - "the social dilemma of autonomous vehicles"

Connected through hyperedge HE-7 (induced by source paper "fast authentication and progressive authorization in large scale iot how to leverage ai for security enhancement"):
  with members:
    - "learning aided physical layer authentication as an intelligent process"

Connected through hyperedge HE-8 (induced by source paper "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"):
  with members:
    - "fast uplink grant for machine type communications challenges and opportunities"
    - "random access schemes in wireless systems with correlated user activity"
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"
    - "grant free massive noma outage probability and throughput"

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.CR(Cryptography and Security)