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: "speech enhancement in multiple noise conditions using deep neural networks"
  Abstract: In this paper we consider the problem of speech enhancement in real-world like conditions where multiple noises can simultaneously corrupt speech. Most of the current literature on speech enhancement focus primarily on presence of single noise in corrupted speech which is far from real-world environments. Specifically, we deal with improving speech quality in office environment where multiple stationary as well as non-stationary noises can be simultaneously present in speech. We propose several strategies based on Deep Neural Networks (DNN) for speech enhancement in these scenarios. We also investigate a DNN training strategy based on psychoacoustic models from speech coding for enhancement of noisy speech

Connected through hyperedge HE-1 (induced by source paper "improving gans for speech enhancement"):
  with members:
    - "towards generalized speech enhancement with generative adversarial networks"
    - "convolutional neural network based speech enhancement for cochlear implant recipients"
    - "segan speech enhancement generative adversarial network"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "a fully convolutional neural network for speech enhancement"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "deep speech inpainting of time frequency masks"):
  with members:
    - "incorporating symbolic sequential modeling for speech enhancement"
    - "spectrogram feature losses for music source separation"
    - "divide and conquer a deep casa approach to talker independent monaural speaker separation"
    - "towards generalized speech enhancement with generative adversarial networks"
    - "a context encoder for audio inpainting"
    - "generative image inpainting with contextual attention"
    - "supervised speech separation based on deep learning an overview"
    - "a hybrid dsp deep learning approach to real time full band speech enhancement"

Connected through hyperedge HE-3 (induced by source paper "coarse to fine optimization for speech enhancement"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "a fully convolutional neural network for speech enhancement"
    - "generative image modeling using style and structure adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-4 (induced by source paper "supervised initialization of lstm networks for fundamental frequency detection in noisy speech signals"):
  with members:
    - "lstm deep neural networks postfiltering for improving the quality of synthetic voices"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-5 (induced by source paper "noise adaptive speech enhancement using domain adversarial training"):
  with members:
    - "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement"
    - "domain adversarial training for accented speech recognition"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "supervised speech separation based on deep learning an overview"
    - "segan speech enhancement generative adversarial network"
    - "adversarial discriminative domain adaptation"
    - "tacotron towards end to end speech synthesis"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"

Connected through hyperedge HE-6 (induced by source paper "on single channel speech enhancement and on non linear modulation domain kalman filtering"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "modulation domain kalman filtering for monaural blind speech denoising and dereverberation"
    - "on the importance of super gaussian speech priors for machine learning based speech enhancement"
    - "model based speech enhancement in the modulation domain"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "speech dereverberation using non negative convolutive transfer function and spectro temporal modeling"
    - "a neural network alternative to non negative audio models"

Connected through hyperedge HE-7 (induced by source paper "modulation domain kalman filtering for monaural blind speech denoising and dereverberation"):
  with members:
    - "on the importance of super gaussian speech priors for machine learning based speech enhancement"
    - "model based speech enhancement in the modulation domain"
    - "on single channel speech enhancement and on non linear modulation domain kalman filtering"
    - "phase aware single channel speech enhancement with modulation domain kalman filtering"

Connected through hyperedge HE-8 (induced by source paper "real time regression analysis with deep convolutional neural networks"):
  with members:
    - "very deep convolutional neural networks for raw waveforms"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image 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.SD(Sound)