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: "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
  Abstract: This study investigates phase reconstruction for deep learning based monaural talker-independent speaker separation in the short-time Fourier transform (STFT) domain. The key observation is that, for a mixture of two sources, with their magnitudes accurately estimated and under a geometric constraint, the absolute phase difference between each source and the mixture can be uniquely determined; in addition, the source phases at each time-frequency (T-F) unit can be narrowed down to only two candidates. To pick the right candidate, we propose three algorithms based on iterative phase reconstruction, group delay estimation, and phase-difference sign prediction. State-of-the-art results are obtained on the publicly available wsj0-2mix and 3mix corpus.

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"
    - "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"
    - "using recurrences in time and frequency within u net architecture for speech enhancement"

Connected through hyperedge HE-2 (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"
    - "universal sound separation"
    - "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 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-3 (induced by source paper "deep filtering signal extraction and reconstruction using complex time frequency filters"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "divide and conquer a deep casa approach to talker independent monaural speaker separation"):
  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"
    - "speaker independent speech separation with deep attractor network"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "multi scale multi band densenets for audio source separation"
    - "regularizing and optimizing lstm language models"
    - "recurrent dropout without memory loss"

Connected through hyperedge HE-5 (induced by source paper "improved speech separation with time and frequency cross domain joint embedding and clustering"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "speaker independent speech separation with deep attractor network"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "deep attractor network for single microphone speaker separation"
    - "segan speech enhancement generative adversarial network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "voxceleb a large scale speaker identification dataset"

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"
    - "universal sound 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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "wham extending speech separation to noisy environments"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "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"
    - "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"
    - "supervised speech separation based on deep learning an overview"
    - "joint separation and denoising of noisy multi talker speech using recurrent neural networks and permutation invariant training"

Connected through hyperedge HE-8 (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"
    - "a comprehensive study of speech separation spectrogram vs waveform separation"
    - "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"

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)