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: "universal sound separation"
  Abstract: Recent deep learning approaches have achieved impressive performance on speech enhancement and separation tasks. However, these approaches have not been investigated for separating mixtures of arbitrary sounds of different types, a task we refer to as universal sound separation, and it is unknown how performance on speech tasks carries over to non-speech tasks. To study this question, we develop a dataset of mixtures containing arbitrary sounds, and use it to investigate the space of mask-based separation architectures, varying both the overall network architecture and the framewise analysis-synthesis basis for signal transformations. These network architectures include convolutional long short-term memory networks and time-dilated convolution stacks inspired by the recent success of time-domain enhancement networks like ConvTasNet. For the latter architecture, we also propose novel modifications that further improve separation performance. In terms of the framewise analysis-synthesis basis, we explore both a short-time Fourier transform (STFT) and a learnable basis, as used in ConvTasNet. For both of these bases, we also examine the effect of window size. In particular, for STFTs, we find that longer windows (25-50 ms) work best for speech/non-speech separation, while shorter windows (2.5 ms) work best for arbitrary sounds. For learnable bases, shorter windows (2.5 ms) work best on all tasks. Surprisingly, for universal sound separation, STFTs outperform learnable bases. Our best methods produce an improvement in scale-invariant signal-to-distortion ratio of over 13 dB for speech/non-speech separation and close to 10 dB for universal sound separation.

Connected through hyperedge HE-1 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis"):
  with members:
    - "audio source separation using variational autoencoders and weak class supervision"
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "panns large scale pretrained audio neural networks for audio pattern recognition"
    - "unsupervised audio source separation via spectrum energy preserved wasserstein learning"
    - "multi scale multi band densenets for audio source separation"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "single channel audio source separation using convolutional denoising autoencoders"

Connected through hyperedge HE-2 (induced by source paper "time domain audio source separation based on wave u net combined with discrete wavelet transform"):
  with members:
    - "making convolutional networks shift invariant again"
    - "end to end music source separation is it possible in the waveform domain"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "end to end sound source separation conditioned on instrument labels"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "end to end source separation with adaptive front ends"
    - "multi scale multi band densenets for audio source separation"
    - "density estimation using real nvp"

Connected through hyperedge HE-3 (induced by source paper "la furca iterative context aware end to end monaural speech separation based on dual path deep parallel inter intra bi lstm with attention"):
  with members:
    - "is cqt more suitable for monaural speech separation than stft an empirical study"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
    - "divide and conquer a deep casa approach to talker independent monaural speaker separation"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "deep attractor network for single microphone speaker separation"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "single channel multi speaker separation using deep clustering"

Connected through hyperedge HE-4 (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"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "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"
    - "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-5 (induced by source paper "improving universal sound separation using sound classification"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "chive varying prosody in speech synthesis with a linguistically driven dynamic hierarchical conditional variational network"
    - "sdr half baked or well done"
    - "speaker independent speech separation with deep attractor network"
    - "class conditional embeddings for music source separation"
    - "differentiable consistency constraints for improved deep speech enhancement"
    - "unsupervised learning of semantic audio representations"
    - "conditional image text embedding networks"

Connected through hyperedge HE-6 (induced by source paper "two step sound source separation training on learned latent targets"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "unsupervised training of a deep clustering model for multichannel blind source separation"
    - "divide and conquer a deep casa approach to talker independent monaural speaker separation"
    - "sdr half baked or well done"
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "end to end non negative autoencoders for sound source separation"):
  with members:
    - "on loss functions for supervised monaural time domain speech enhancement"
    - "phasebook and friends leveraging discrete representations for source separation"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "sdr half baked or well done"
    - "a variational autoencoder for probabilistic non negative matrix factorisation"
    - "end to end source separation with adaptive front ends"
    - "tasnet time domain audio separation network for real time single channel speech separation"

Connected through hyperedge HE-8 (induced by source paper "alternating between spectral and spatial estimation for speech separation and enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "unsupervised speech enhancement based on multichannel nmf informed beamforming for noise robust automatic speech recognition"
    - "libritts a corpus derived from librispeech for text to speech"
    - "sdr half baked or well done"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "differentiable consistency constraints for improved deep speech enhancement"
    - "supervised speech separation based on deep learning an overview"

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)