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: "furcanext end to end monaural speech separation with dynamic gated dilated temporal convolutional networks"
  Abstract: Deep dilated temporal convolutional networks (TCN) have been proved to be very effective in sequence modeling. In this paper we propose several improvements of TCN for end-to-end approach to monaural speech separation, which consists of 1) multi-scale dynamic weighted gated dilated convolutional pyramids network (FurcaPy), 2) gated TCN with intra-parallel convolutional components (FurcaPa), 3) weight-shared multi-scale gated TCN (FurcaSh), 4) dilated TCN with gated difference-convolutional component (FurcaSu), that all these networks take the mixed utterance of two speakers and maps it to two separated utterances, where each utterance contains only one speaker's voice. For the objective, we propose to train the network by directly optimizing utterance level signal-to-distortion ratio (SDR) in a permutation invariant training (PIT) style. Our experiments on the the public WSJ0-2mix data corpus results in 18.4dB SDR improvement, which shows our proposed networks can leads to performance improvement on the speaker separation task.

Connected through hyperedge HE-1 (induced by source paper "continuous speech separation dataset and analysis"):
  with members:
    - "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"
    - "a comprehensive study of speech separation spectrogram vs waveform separation"
    - "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-2 (induced by source paper "divide and conquer a deep casa approach to talker independent monaural speaker separation"):
  with members:
    - "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 learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "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-3 (induced by source paper "wham extending speech separation to noisy environments"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "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"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "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-4 (induced by source paper "multi channel speech separation using deep embedding model with multilayer bootstrap networks"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel 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 "whamr noisy and reverberant single channel speech separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "wham extending speech separation to noisy environments"
    - "sdr half baked or well done"
    - "end to end monaural multi speaker asr system without pretraining"
    - "a purely end to end system for multi speaker speech recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "single channel multi speaker separation using deep clustering"

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)