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: "segan speech enhancement generative adversarial network"
  Abstract: Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.

Connected through hyperedge HE-1 (induced by source paper "when relation networks meet gans relation gans with triplet loss"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "improving generalization and stability of generative adversarial networks"
    - "gan qp a novel gan framework without gradient vanishing and lipschitz constraint"
    - "the relativistic discriminator a key element missing from standard gan"
    - "deep video generation prediction and completion of human action sequences"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"

Connected through hyperedge HE-2 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "on the relationship between short time objective intelligibility and short time spectral amplitude mean square error for speech enhancement"
    - "on the variance of the adaptive learning rate and beyond"
    - "a comprehensive study of speech separation spectrogram vs waveform separation"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "sdr half baked or well done"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"

Connected through hyperedge HE-3 (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"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "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-4 (induced by source paper "a dual staged context aggregation method towards efficient end to end speech enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "incorporating symbolic sequential modeling for speech enhancement"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on cross corpus generalization of deep learning based speech enhancement"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "voxceleb2 deep speaker recognition"
    - "supervised speech separation based on deep learning an overview"
    - "a wavenet for speech denoising"
    - "adam a method for stochastic optimization"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

Connected through hyperedge HE-6 (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-7 (induced by source paper "earthquakegen earthquake simulation using generative adversarial networks"):
  with members:
    - "deepdetect a cascaded region based densely connected network for seismic event detection"
    - "gansynth adversarial neural audio synthesis"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "large scale gan training for high fidelity natural image synthesis"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

Connected through hyperedge HE-8 (induced by source paper "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"):
  with members:
    - "unsupervised singing voice conversion"
    - "a rad approach to deep mixture models"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "conditional recurrent flow conditional generation of longitudinal samples with applications to neuroimaging"
    - "neural voice cloning with a few samples"

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)