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: "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation"
  Abstract: Deep gated convolutional networks have been proved to be very effective in single channel speech separation. However current state-of-the-art framework often considers training the gated convolutional networks in time-frequency (TF) domain. Such an approach will result in limited perceptual score, such as signal-to-distortion ratio (SDR) upper bound of separated utterances and also fail to exploit an end-to-end framework. In this paper we present an integrated simple and effective end-to-end approach to monaural speech separation, which consists of deep gated convolutional neural networks (GCNN) that takes the mixed utterance of two speakers and maps it to two separated utterances, where each utterance contains only one speaker's voice. In addition long short-term memory (LSTM) is employed for long term temporal modeling. For the objective, we propose to train the network by directly optimizing utterance level SDR in a permutation invariant training (PIT) style. Our experiments on the public WSJ0-2mix data corpus demonstrate that this new scheme can produce more discriminative separated utterances and leading to performance improvement on the speaker separation task.

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"
    - "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-2 (induced by source paper "a style transfer approach to source separation"):
  with members:
    - "cyclegan vc2 improved cyclegan based non parallel voice conversion"
    - "unsupervised training of a deep clustering model for multichannel blind source separation"
    - "unsupervised training of neural mask based beamforming"
    - "audio style transfer"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "sdr half baked or well done"
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"

Connected through hyperedge HE-3 (induced by source paper "furcanext end to end monaural speech separation with dynamic gated dilated temporal convolutional networks"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "speaker independent speech separation with deep attractor network"
    - "accurate data efficient unconstrained text recognition with convolutional neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "deep attractor network for single microphone speaker separation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "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 "multi channel speech separation using deep embedding model with multilayer bootstrap networks"):
  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"
    - "discriminative learning for monaural speech separation using deep embedding features"
    - "speaker independent speech separation with deep attractor network"
    - "end to end source separation with adaptive front ends"
    - "deep attractor network for single microphone speaker separation"
    - "supervised speech separation based on deep learning an overview"
    - "tasnet time domain audio separation network for real time single channel speech separation"

Connected through hyperedge HE-5 (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"
    - "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)