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: "a comprehensive study of speech separation spectrogram vs waveform separation"
  Abstract: Speech separation has been studied widely for single-channel close-talk recordings over the past few years; developed solutions are mostly in frequency-domain. Recently, a raw audio waveform separation network (TasNet) introduced for single-channel data, with achieving high Si-SNR (scale-invariant source-to-noise ratio) and SDR (source-to-distortion ratio) comparing against the state-of-the-art solution in frequency-domain. In this study, we incorporate effective components of TasNet into a frequency-domain separation method. We compare both for alternative scenarios. We introduce a solution for directly optimizing the separation criterion in frequency-domain networks. In addition to speech separation objective and subjective measurements, we evaluate the separation performance on a speech recognition task as well. We study the speech separation problem for far-filed data (more similar to naturalistic audio streams) and develop multi-channel solutions for both frequency and time-domain separators with utilizing spectral, spatial and speaker location information. For our experiments, we simulated multi-channel spatialized reverberate WSJ0-2mix dataset. Our experimental results show that spectrogram separation can achieve competitive performance with better network design. With multi-channel framework as well, we can obtain relatively up to +35.5% and +46% improvement in terms of WER and SDR, respectively.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "simultaneous speech recognition and speaker diarization for monaural dialogue recordings with target speaker acoustic models"
    - "wham extending speech separation to noisy environments"
    - "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-3 (induced by source paper "a unified framework for speech separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "end to end multi channel speech separation"
    - "single channel speech separation with auxiliary speaker embeddings"
    - "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"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "end to end source separation with adaptive front ends"
    - "supervised speech separation based on deep learning an overview"

Connected through hyperedge HE-4 (induced by source paper "demystifying tasnet a dissecting approach"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "wham extending speech separation to noisy environments"
    - "sms wsj database performance measures and baseline recipe for multi channel source separation and recognition"
    - "end to end multi channel speech separation"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
    - "recursive speech separation for unknown number of speakers"
    - "end to end speech separation with unfolded iterative phase reconstruction"

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)