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: "divide and conquer a deep casa approach to talker independent monaural speaker separation"
  Abstract: We address talker-independent monaural speaker separation from the perspectives of deep learning and computational auditory scene analysis (CASA). Specifically, we decompose the multi-speaker separation task into the stages of simultaneous grouping and sequential grouping. Simultaneous grouping is first performed in each time frame by separating the spectra of different speakers with a permutation-invariantly trained neural network. In the second stage, the frame-level separated spectra are sequentially grouped to different speakers by a clustering network. The proposed deep CASA approach optimizes frame-level separation and speaker tracking in turn, and produces excellent results for both objectives. Experimental results on the benchmark WSJ0-2mix database show that the new approach achieves the state-of-the-art results with a modest model size.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "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-3 (induced by source paper "deep speech inpainting of time frequency masks"):
  with members:
    - "incorporating symbolic sequential modeling for speech enhancement"
    - "spectrogram feature losses for music source separation"
    - "towards generalized speech enhancement with generative adversarial networks"
    - "a context encoder for audio inpainting"
    - "generative image inpainting with contextual attention"
    - "supervised speech separation based on deep learning an overview"
    - "a hybrid dsp deep learning approach to real time full band speech enhancement"
    - "speech enhancement in multiple noise conditions using deep neural networks"

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)