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: "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.

Connected through hyperedge HE-1 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation"):
  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"
    - "cracking the cocktail party problem by multi beam deep attractor network"
    - "deep extractor network for target speaker recovery from single channel speech mixtures"
    - "end to end source separation with adaptive front ends"

Connected through hyperedge HE-2 (induced by source paper "cgcnn complex gabor convolutional neural network on raw speech"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "end to end speech recognition from the raw waveform"
    - "improving speech recognition by revising gated recurrent units"
    - "deep complex networks"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "end to end phoneme sequence recognition using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "on cross corpus generalization of deep learning based speech enhancement"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "supervised speech separation based on deep learning an overview"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "adam a method for stochastic optimization"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

Connected through hyperedge HE-4 (induced by source paper "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "learning with learned loss function speech enhancement with quality net to improve perceptual evaluation of speech quality"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "spectral feature mapping with mimic loss for robust speech recognition"
    - "spectral normalization for generative adversarial networks"
    - "quality net an end to end non intrusive speech quality assessment model based on blstm"
    - "noise adaptive speech enhancement using domain adversarial training"

Connected through hyperedge HE-5 (induced by source paper "improved speech separation with time and frequency cross domain joint embedding and clustering"):
  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"
    - "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 "end to end multi channel speech separation"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "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"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "end to end source separation with adaptive front ends"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"

Connected through hyperedge HE-7 (induced by source paper "coarse to fine optimization for speech enhancement"):
  with members:
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"
    - "speech enhancement in multiple noise conditions using deep neural networks"
    - "a fully convolutional neural network for speech enhancement"
    - "generative image modeling using style and structure adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-8 (induced by source paper "end to end multi task denoising for joint sdr and pesq optimization"):
  with members:
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "sdr half baked or well done"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "segan speech enhancement generative adversarial network"
    - "a wavenet for speech denoising"

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)