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 "learning with learned loss function speech enhancement with quality net to improve perceptual evaluation of speech quality"):
  Abstract: Utilizing a human-perception-related objective function to train a speech enhancement model has become a popular topic recently. The main reason is that the conventional mean squared error (MSE) loss cannot represent auditory perception well. One of the typical hu-man-perception-related metrics, which is the perceptual evaluation of speech quality (PESQ), has been proven to provide a high correlation to the quality scores rated by humans. Owing to its complex and non-differentiable properties, however, the PESQ function may not be used to optimize speech enhancement models directly. In this study, we propose optimizing the enhancement model with an approximated PESQ function, which is differentiable and learned from the training data. The experimental results show that the learned surrogate function can guide the enhancement model to further boost the PESQ score (in-crease of 0.18 points compared to the results trained with MSE loss) and maintain the speech intelligibility.

Includes members:
  Member "conv tasnet surpassing ideal time frequency magnitude masking for speech 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 "end to end speech separation with unfolded iterative phase reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "quality net an end to end non intrusive speech quality assessment model based on blstm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "comparison of speech representations for automatic quality estimation in multi speaker text to speech synthesis")
  Member "deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "raw multi channel audio source separation using multi resolution convolutional auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement")
  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 "segan speech enhancement generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "when relation networks meet gans relation gans with triplet loss")

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)