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: "over the air deep learning based radio signal classification"
  Abstract: We conduct an in  depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification, and compare performance between the two approaches across a range of configurations and channel impairments. We consider the effects of carrier frequency offset, symbol rate, and multipath fading in simulation, and conduct over-the-air measurement of radio classification performance in the lab using software radios, and we compare performance and training strategies for both. Finally, we conclude with a discussion of remaining problems, and design considerations for using such techniques.

Connected through hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "realtime scheduling and power allocation using deep neural networks"
    - "active learning for wireless iot intrusion detection"
    - "electrosense open and big spectrum data"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "adversarial attacks on deep learning based radio signal classification"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-2 (induced by source paper "machine learning for wireless communications in the internet of things a comprehensive survey"):
  with members:
    - "helper heterogeneous efficient low power radio for enabling ad hoc emergency public safety networks"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "a non parametric multi stage learning framework for cognitive spectrum access in iot networks"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "cognitive non orthogonal multiple access with cooperative relaying a new wireless frontier for 5g spectrum sharing"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "fog computing survey of trends architectures requirements and research directions"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-3 (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"
    - "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-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"
    - "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-5 (induced by source paper "big data goes small real time spectrum driven embedded wireless networking through deep learning in the rf loop"):
  with members:
    - "cognitive non orthogonal multiple access with cooperative relaying a new wireless frontier for 5g spectrum sharing"
    - "an introduction to deep learning for the physical layer"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "learning to detect and mitigate cross layer attacks in wireless networks framework and applications"
    - "deep architectures for modulation recognition"
    - "end to end learning from spectrum data a deep learning approach for wireless signal identification in spectrum monitoring applications"
    - "identity mappings in deep residual networks"
    - "spectrum sharing in mmwave cellular networks via cell association coordination and beamforming"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples in rf deep learning detection of the attack and its physical robustness"):
  with members:
    - "certified defenses against adversarial examples"
    - "adversarial attacks on deep learning based radio signal classification"
    - "an introduction to deep learning for the physical layer"
    - "robust physical world attacks on deep learning models"
    - "convolutional radio modulation recognition networks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning for large scale real world acars and ads b radio signal classification"):
  with members:
    - "electrosense open and big spectrum data"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "deep learning in mobile and wireless networking a survey"
    - "an introduction to deep learning for the physical layer"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "convolutional radio modulation recognition networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "robust deep sensing through transfer learning in cognitive radio"):
  with members:
    - "generative adversarial learning for spectrum sensing"
    - "deep learning for launching and mitigating wireless jamming attacks"
    - "deep sensing cooperative spectrum sensing based on convolutional neural networks"
    - "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"

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)