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 architectures for modulation recognition"
  Abstract: We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that radio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods.

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"
    - "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"

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"
    - "over the air deep learning based radio signal classification"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "big data goes small real time spectrum driven embedded wireless networking through deep learning in the rf loop"):
  with members:
    - "over the air deep learning based radio signal classification"
    - "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"
    - "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-4 (induced by source paper "communication without interception defense against deep learning based modulation detection"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "an introduction to deep learning for the physical layer"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "practical black box attacks against machine learning"
    - "adversarial machine learning at scale"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep learning for spectrum sensing"):
  with members:
    - "deep learning for wireless physical layer opportunities and challenges"
    - "analysis of spectrum occupancy using machine learning algorithms"
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"

Connected through hyperedge HE-6 (induced by source paper "over the air deep learning based radio signal classification"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "self normalizing neural networks"
    - "xgboost a scalable tree boosting system"
    - "convolutional radio modulation recognition networks"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "blind signal classification for non orthogonal multiple access in vehicular networks"):
  with members:
    - "blind signal classification analysis and impact on user scheduling and power allocation in nonorthogonal multiple access"
    - "bi directional cooperative noma without full csit"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "v2x meets noma non orthogonal multiple access for 5g enabled vehicular networks"
    - "noma based calibration for large scale spaceborne antenna arrays"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

Connected through hyperedge HE-8 (induced by source paper "key technologies and system trade offs for detection and localization of amateur drones"):
  with members:
    - "electrosense open and big spectrum data"
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "flight dynamics based recovery of a uav trajectory using ground cameras"
    - "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"

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)