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: "end to end optimized speech coding with deep neural networks"
  Abstract: Modern compression algorithms are often the result of laborious domain-specific research; industry standards such as MP3, JPEG, and AMR-WB took years to develop and were largely hand-designed. We present a deep neural network model which optimizes all the steps of a wideband speech coding pipeline (compression, quantization, entropy coding, and decompression) end-to-end directly from raw speech data -- no manual feature engineering necessary, and it trains in hours. In testing, our DNN-based coder performs on par with the AMR-WB standard at a variety of bitrates (~9kbps up to ~24kbps). It also runs in realtime on a 3.8GhZ Intel CPU.

Connected through hyperedge HE-1 (induced by source paper "machine learning in the air"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "federated learning over wireless fading channels"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-2 (induced by source paper "efficient context aggregation for end to end speech enhancement using a densely connected convolutional and recurrent network"):
  with members:
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "densely connected convolutional networks"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "low bit rate speech coding with vq vae and a wavenet decoder"):
  with members:
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "sample efficient adaptive text to speech"
    - "neural discrete representation learning"
    - "end to end optimized image compression"
    - "composition of deep and spiking neural networks for very low bit rate speech coding"
    - "wavenet a generative model for raw audio"
    - "speex a free codec for free speech"

Connected through hyperedge HE-4 (induced by source paper "automatic detection and compression for passive acoustic monitoring of the african forest elephant"):
  with members:
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "end to end optimized image compression"

Connected through hyperedge HE-5 (induced by source paper "deep generative models for distribution preserving lossy compression"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "video compression through image interpolation"
    - "towards image understanding from deep compression without decoding"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep generative adversarial compression artifact removal"

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.SD(Sound)