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 "deep speech inpainting of time frequency masks"):
  Abstract: In particularly noisy environments, transient loud intrusions can completely overpower parts of the speech signal, leading to an inevitable loss of information. Recent algorithms for noise suppression often yield impressive results but tend to struggle when the signal-to-noise ratio (SNR) of the mixture is low or when parts of the signal are missing. To address these issues, here we introduce an end-to-end framework for the retrieval of missing or severely distorted parts of time-frequency representation of speech, from the short-term context, thus speech inpainting. The framework is based on a convolutional U-Net trained via deep feature losses, obtained through speechVGG, a deep speech feature extractor pre-trained on the word classification task. Our evaluation results demonstrate that the proposed framework is effective at recovering large portions of missing or distorted parts of speech. Specifically, it yields notable improvements in STOI & PESQ objective metrics, as assessed using the LibriSpeech dataset.

Includes members:
  Member "incorporating symbolic sequential modeling for speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a dual staged context aggregation method towards efficient end to end speech enhancement")
  Member "spectrogram feature losses for music source separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "speech vgg a deep feature extractor for speech processing")
  Member "divide and conquer a deep casa approach to talker independent monaural speaker separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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")
  Member "towards generalized speech enhancement with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving gans for speech enhancement")
  Member "a context encoder for audio inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep long audio inpainting")
  Member "generative image inpainting with contextual attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "supervised speech separation based on deep learning an overview":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "a hybrid dsp deep learning approach to real time full band speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a scalable noisy speech dataset and online subjective test framework")

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)