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: "exploring speech enhancement with generative adversarial networks for robust speech recognition"
  Abstract: We investigate the effectiveness of generative adversarial networks (GANs) for speech enhancement, in the context of improving noise robustness of automatic speech recognition (ASR) systems. Prior work demonstrates that GANs can effectively suppress additive noise in raw waveform speech signals, improving perceptual quality metrics; however this technique was not justified in the context of ASR. In this work, we conduct a detailed study to measure the effectiveness of GANs in enhancing speech contaminated by both additive and reverberant noise. Motivated by recent advances in image processing, we propose operating GANs on log-Mel filterbank spectra instead of waveforms, which requires less computation and is more robust to reverberant noise. While GAN enhancement improves the performance of a clean-trained ASR system on noisy speech, it falls short of the performance achieved by conventional multi-style training (MTR). By appending the GAN-enhanced features to the noisy inputs and retraining, we achieve a 7% WER improvement relative to the MTR system.

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"
    - "speech enhancement in multiple noise conditions using deep neural networks"
    - "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 learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"

Connected through hyperedge HE-3 (induced by source paper "sinusoidal wave generating network based on adversarial learning and its application synthesizing frog sounds for data augmentation"):
  with members:
    - "joint alignment of multiple point sets with batch and incremental expectation maximization"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "began boundary equilibrium generative adversarial networks"
    - "generative cooperative net for image generation and data augmentation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "improved training of wasserstein gans"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-4 (induced by source paper "probabilistic forecasting of sensory data with generative adversarial networks forgan"):
  with members:
    - "phaselink a deep learning approach to seismic phase association"
    - "predicting inpatient discharge prioritization with electronic health records"
    - "deepdownscale a deep learning strategy for high resolution weather forecast"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "voice impersonation using generative adversarial networks"
    - "cred a deep residual network of convolutional and recurrent units for earthquake signal detection"
    - "svsgan singing voice separation via generative adversarial network"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-5 (induced by source paper "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "learning with learned loss function speech enhancement with quality net to improve perceptual evaluation of speech quality"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "spectral feature mapping with mimic loss for robust speech recognition"
    - "spectral normalization for generative adversarial networks"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"

Connected through hyperedge HE-6 (induced by source paper "towards generalized speech enhancement with generative adversarial networks"):
  with members:
    - "high fidelity image generation with fewer labels"
    - "clarinet parallel wave generation in end to end text to speech"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "language and noise transfer in speech enhancement generative adversarial network"
    - "segan speech enhancement generative adversarial network"

Connected through hyperedge HE-7 (induced by source paper "unpaired speech enhancement by acoustic and adversarial supervision for speech recognition"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "a network of deep neural networks for distant speech recognition"
    - "segan speech enhancement generative adversarial network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a wavenet for speech denoising"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "deep speech enhancement for reverberated and noisy signals using wide residual networks"):
  with members:
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "convolutional recurrent neural networks for speech enhancement"
    - "segan speech enhancement generative adversarial network"
    - "decoupled weight decay regularization"
    - "a fully convolutional neural network for speech enhancement"
    - "wide residual networks"
    - "musan a music speech and noise corpus"

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)