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 models for wireless signal classification with distributed low cost spectrum sensors"
  Abstract: This paper looks into the modulation classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for automatic modulation classification based on long short term memory (LSTM) is proposed. The model learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments. Analyses show that the proposed model yields an average classification accuracy of close to 90% at varying signal-to-noise ratio conditions ranging from 0 dB to 20 dB. Further, we explore the utility of this LSTM model for a variable symbol rate scenario. We show that a LSTM based model can learn good representations of variable length time domain sequences, which is useful in classifying modulation signals with different symbol rates. The achieved accuracy of 75% on an input sample length of 64 for which it was not trained, substantiates the representation power of the model. To reduce the data communication overhead from distributed sensors, the feasibility of classification using averaged magnitude spectrum data and on-line classification on the low-cost spectrum sensors are studied. Furthermore, quantized realizations of the proposed models are analyzed for deployment on sensors with low processing power.

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"
    - "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 "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"
    - "detection algorithms for communication systems using deep learning"

Connected through hyperedge HE-3 (induced by source paper "deep complex networks for protocol agnostic radio frequency device fingerprinting in the wild"):
  with members:
    - "deepradioid real time channel resilient optimization of deep learning based radio fingerprinting algorithms"
    - "gated complex recurrent neural networks"
    - "complex gated recurrent neural networks"
    - "an introduction to deep learning for the physical layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "deep complex networks"
    - "image augmentation using radial transform for training deep neural networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-4 (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"
    - "generative adversarial learning for spectrum sensing"
    - "a model driven deep learning network for mimo detection"

Connected through hyperedge HE-5 (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"
    - "electrosense open and big spectrum data"
    - "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-6 (induced by source paper "deep learning for cross technology communication design"):
  with members:
    - "deep learning in mobile and wireless networking a survey"
    - "an introduction to deep learning for the physical layer"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "free side channel cross technology communication in wireless networks"
    - "deep learning based mimo communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "hardware aware machine learning modeling and optimization"):
  with members:
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "designing adaptive neural networks for energy constrained image classification"
    - "mnasnet platform aware neural architecture search for mobile"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "neuralpower predict and deploy energy efficient convolutional neural networks"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "lightnn filling the gap between conventional deep neural networks and binarized networks"

Connected through hyperedge HE-8 (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"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "deep architectures for modulation recognition"
    - "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"

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)