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 sensing cooperative spectrum sensing based on convolutional neural networks"
  Abstract: In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user (PU) which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS which is hard to compute and also hard to use in practice, the strategy for combining the individual sensing results of the SUs is learned with a CNN using training sensing samples. Accordingly, an environment-specific CSS which considers both spectral and spatial correlation of individual sensing outcomes, is found in an adaptive manner regardless of whether the individual sensing results are quantized or not. Through simulation, we show that the performance of CSS can be improved by the proposed DCS with low complexity even when the number of training samples is moderate.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "spectrum data poisoning with adversarial deep 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"

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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep learning for rf signal classification in unknown and dynamic spectrum environments"):
  with members:
    - "spectrum data poisoning with adversarial deep learning"
    - "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"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-5 (induced by source paper "deepwifi cognitive wifi with deep learning"):
  with members:
    - "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"
    - "enabling multi hop ad hoc networks through wifi direct multi group networking"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "convolutional radio modulation recognition networks"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "robust deep sensing through transfer learning in cognitive radio"):
  with members:
    - "over the air deep learning based radio signal classification"
    - "generative adversarial learning for spectrum sensing"
    - "deep learning for launching and mitigating wireless jamming attacks"
    - "end to end learning for self driving cars"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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"
    - "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"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "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)