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: "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
  Abstract: Single-channel, speaker-independent speech separation methods have recently seen great progress. However, the accuracy, latency, and computational cost of such methods remain insufficient. The majority of the previous methods have formulated the separation problem through the time–frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time–frequency representation for speech separation, and the long latency in calculating the spectrograms. To address these shortcomings, we propose a fully convolutional time-domain audio separation network Conv-TasNet, a deep learning framework for end-to-end time-domain speech separation. Conv-TasNet uses a linear encoder to generate a representation of the speech waveform optimized for separating individual speakers. Speaker separation is achieved by applying a set of weighting functions masks to the encoder output. The modified encoder representations are then inverted back to the waveforms using a linear decoder. The masks are found using a temporal convolutional network consisting of stacked one-dimensional dilated convolutional blocks, which allows the network to model the long-term dependencies of the speech signal while maintaining a small model size. The proposed Conv-TasNet system significantly outperforms previous time–frequency masking methods in separating two- and three-speaker mixtures. Additionally, Conv-TasNet surpasses several ideal time–frequency magnitude masks in two-speaker speech separation as evaluated by both objective distortion measures and subjective quality assessment by human listeners. Finally, Conv-TasNet has a significantly smaller model size and a shorter minimum latency, making it a suitable solution for both offline and real-time speech separation applications. This study, therefore, represents a major step toward the realization of speech separation systems for real-world speech processing technologies.

Connected through hyperedge HE-1 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "phase aware speech enhancement with deep complex u net"
    - "sdr half baked or well done"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "cracking the cocktail party problem by multi beam deep attractor network"
    - "deep extractor network for target speaker recovery from single channel speech mixtures"
    - "end to end source separation with adaptive front ends"

Connected through hyperedge HE-2 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement"):
  with members:
    - "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"
    - "using recurrences in time and frequency within u net architecture for speech enhancement"

Connected through hyperedge HE-3 (induced by source paper "continuous speech separation dataset and analysis"):
  with members:
    - "furcanext end to end monaural speech separation with dynamic gated dilated temporal convolutional networks"
    - "simultaneous speech recognition and speaker diarization for monaural dialogue recordings with target speaker acoustic models"
    - "wham extending speech separation to noisy environments"
    - "a comprehensive study of speech separation spectrogram vs waveform separation"
    - "pykaldi2 yet another speech toolkit based on kaldi and pytorch"
    - "sdr half baked or well done"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"

Connected through hyperedge HE-4 (induced by source paper "meta learning extractors for music source separation"):
  with members:
    - "audio query based music source separation"
    - "on the variance of the adaptive learning rate and beyond"
    - "recursive speech separation for unknown number of speakers"
    - "lookahead optimizer k steps forward 1 step back"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "meta learning a dynamical language model"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "a dual staged context aggregation method towards efficient end to end speech enhancement"):
  with members:
    - "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"
    - "segan speech enhancement generative adversarial network"
    - "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-6 (induced by source paper "the interspeech 2020 deep noise suppression challenge datasets subjective speech quality and testing framework"):
  with members:
    - "a scalable noisy speech dataset and online subjective test framework"
    - "phasen a phase and harmonics aware speech enhancement network"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "an individualized super gaussian single microphone speech enhancement for hearing aid users with smartphone as an assistive device"

Connected through hyperedge HE-7 (induced by source paper "finding strength in weakness learning to separate sounds with weak supervision"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "audio source separation using variational autoencoders and weak class supervision"
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "cutting music source separation some slakh a dataset to study the impact of training data quality and quantity"
    - "universal sound separation"
    - "co separating sounds of visual objects"
    - "sdr half baked or well done"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"

Connected through hyperedge HE-8 (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"
    - "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"
    - "noise adaptive speech enhancement using domain adversarial training"

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)