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 "comparison of speech representations for automatic quality estimation in multi speaker text to speech synthesis"):
  Abstract: We aim to characterize how different speakers contribute to the perceived output quality of multi-speaker Text-to-Speech (TTS) synthesis. We automatically rate the quality of TTS using a neural network (NN) trained on human mean opinion score (MOS) ratings. First, we train and evaluate our NN model on 13 different TTS and voice conversion (VC) systems from the ASVSpoof 2019 Logical Access (LA) Dataset. Since it is not known how best to represent speech for this task, we compare 8 different representations alongside MOSNet frame-based features. Our representations include image-based spectrogram features and x-vector embeddings that explicitly model different types of noise such as T60 reverberation time. Our NN predicts MOS with a high correlation to human judgments. We report prediction correlation and error. A key finding is the quality achieved for certain speakers seems consistent, regardless of the TTS or VC system. It is widely accepted that some speakers give higher quality than others for building a TTS system: our method provides an automatic way to identify such speakers. Finally, to see if our quality prediction models generalize, we predict quality scores for synthetic speech using a separate multi-speaker TTS system that was trained on LibriTTS data, and conduct our own MOS listening test to compare human ratings with our NN predictions.

Includes members:
  Member "mosnet deep learning based objective assessment for voice conversion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "moevc a mixture of experts voice conversion system with sparse gating mechanism for accelerating online computation")
  Member "speech replay detection with x vector attack embeddings and spectral features":
    also connected through other hyperedges:
      (none)
  Member "embeddings for dnn speaker adaptive training":
    also connected through other hyperedges:
      (none)
  Member "libritts a corpus derived from librispeech for text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "espnet tts unified reproducible and integratable open source end to end text to speech toolkit")
  Member "analyzing deep cnn based utterance embeddings for acoustic model adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "contextual phonetic pretraining for end to end utterance level language and speaker recognition")
  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 "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement")
  Member "efficiently trainable text to speech system based on deep convolutional networks with guided attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "moglow probabilistic and controllable motion synthesis using normalising flows")
  Member "automos learning a non intrusive assessor of naturalness of speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mosnet deep learning based objective assessment for voice conversion")

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.CL(Computation and Language)