human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "phase aware speech enhancement with deep complex u net"):
  Abstract: Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction. This is due to the difficulty of estimating the phase of clean speech. To improve speech enhancement performance, we tackle the phase estimation problem in three ways. First, we propose Deep Complex U-Net, an advanced U-Net structured model incorporating well-defined complex-valued building blocks to deal with complex-valued spectrograms. Second, we propose a polar coordinate-wise complex-valued masking method to reflect the distribution of complex ideal ratio masks. Third, we define a novel loss function, weighted source-to-distortion ratio (wSDR) loss, which is designed to directly correlate with a quantitative evaluation measure. Our model was evaluated on a mixture of the Voice Bank corpus and DEMAND database, which has been widely used by many deep learning models for speech enhancement. Ablation experiments were conducted on the mixed dataset showing that all three proposed approaches are empirically valid. Experimental results show that the proposed method achieves state-of-the-art performance in all metrics, outperforming previous approaches by a large margin.

Includes members:
  Member "phasebook and friends leveraging discrete representations for source separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "looking to listen at the cocktail party a speaker independent audio visual model for speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time domain audio source separation based on wave u net combined with discrete wavelet transform")
  Member "wave u net a multi scale neural network for end to end audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "source separation with deep generative priors")
  Member "nsml meet the mlaas platform with a real world case study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an empirical evaluation on robustness and uncertainty of regularization methods")
  Member "segan speech enhancement generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "deep complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep quaternion features for privacy protection")
  Member "a wavenet for speech denoising":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bayesian neural networks with maximum mean discrepancy regularization")

Question: Please tell me which class the source paper 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)