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: "electrosense open and big spectrum data"
  Abstract: While radio spectrum allocation is well regulated, there is little knowledge about its actual utilization over time and space. This limitation hinders taking effective actions in various applications including cognitive radios, electrosmog monitoring, and law enforcement. We introduce Electrosense, an initiative that seeks a more efficient, safe and reliable monitoring of the electromagnetic space by improving the accessibility of spectrum data for the general public. A collaborative spectrum monitoring network is designed that monitors the spectrum at large scale with low-cost spectrum sensing nodes. The large set of data is stored and processed in a big data architecture and provided back to the community with an open spectrum data as a service model, that allows users to build diverse and novel applications with different requirements. We illustrate useful usage scenarios of the Electrosense data.

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"
    - "over the air deep learning based radio signal classification"
    - "active learning for wireless iot intrusion detection"
    - "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 "deep learning for large scale real world acars and ads b radio signal classification"):
  with members:
    - "over the air deep learning based radio signal classification"
    - "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-3 (induced by source paper "machine learning for link quality estimation a survey"):
  with members:
    - "machine learning based link fault identification and localization in complex networks"
    - "over the air deep learning based radio signal classification"
    - "deep learning methods for improved decoding of linear codes"
    - "learning the mmse channel estimator"
    - "deep learning in mobile and wireless networking a survey"
    - "effective capacity in wireless networks a comprehensive survey"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"

Connected through hyperedge HE-4 (induced by source paper "key technologies and system trade offs for detection and localization of amateur drones"):
  with members:
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "flight dynamics based recovery of a uav trajectory using ground cameras"
    - "deep architectures for modulation recognition"
    - "localization in long range ultra narrow band iot networks using rssi"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "convolutional radio modulation recognition networks"

Connected through hyperedge HE-5 (induced by source paper "deep learning models for wireless signal classification with distributed low cost spectrum sensors"):
  with members:
    - "deep architectures for modulation recognition"
    - "convolutional radio modulation recognition networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "automatic detection and query of wireless spectrum events from streaming data"):
  with members:
    - "spectrum monitoring for radar bands using deep convolutional neural networks"
    - "signal identification for adaptive spectrum hyperspace access in wireless communications systems"

Connected through hyperedge HE-7 (induced by source paper "electrosense empowering people to decode the radio spectrum"):
  with members:
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"

Connected through hyperedge HE-8 (induced by source paper "end to end learning from spectrum data a deep learning approach for wireless signal identification in spectrum monitoring applications"):
  with members:
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "spectrum monitoring for radar bands using deep convolutional neural networks"
    - "wireless interference identification with convolutional neural networks"
    - "hardware accelerated sdr platform for adaptive air interfaces"
    - "will sdn be part of 5g"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "demo wiscop wireless sensor communication prototyping platform"

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)