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: "quality net an end to end non intrusive speech quality assessment model based on blstm"
  Abstract: Nowadays, most of the objective speech quality assessment tools (e.g., perceptual evaluation of speech quality (PESQ)) are based on the comparison of the degraded/processed speech with its clean counterpart. The need of a "golden" reference considerably restricts the practicality of such assessment tools in real-world scenarios since the clean reference usually cannot be accessed. On the other hand, human beings can readily evaluate the speech quality without any reference (e.g., mean opinion score (MOS) tests), implying the existence of an objective and non-intrusive (no clean reference needed) quality assessment mechanism. In this study, we propose a novel end-to-end, non-intrusive speech quality evaluation model, termed Quality-Net, based on bidirectional long short-term memory. The evaluation of utterance-level quality in Quality-Net is based on the frame-level assessment. Frame constraints and sensible initializations of forget gate biases are applied to learn meaningful frame-level quality assessment from the utterance-level quality label. Experimental results show that Quality-Net can yield high correlation to PESQ (0.9 for the noisy speech and 0.84 for the speech processed by speech enhancement). We believe that Quality-Net has potential to be used in a wide variety of applications of speech signal processing.

Connected through hyperedge HE-1 (induced by source paper "comparison of speech representations for automatic quality estimation in multi speaker text to speech synthesis"):
  with members:
    - "mosnet deep learning based objective assessment for voice conversion"
    - "speech replay detection with x vector attack embeddings and spectral features"
    - "embeddings for dnn speaker adaptive training"
    - "libritts a corpus derived from librispeech for text to speech"
    - "analyzing deep cnn based utterance embeddings for acoustic model adaptation"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "automos learning a non intrusive assessor of naturalness of speech"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "metricgan generative adversarial networks based black box metric scores optimization for speech enhancement"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "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"
    - "noise adaptive speech enhancement using domain adversarial training"

Connected through hyperedge HE-3 (induced by source paper "learning to model aspects of hearing perception using neural loss functions"):
  with members:
    - "end to end spoken language translation"
    - "audio linguistic embeddings for spoken sentences"
    - "neural style transfer for audio spectograms"
    - "conditional end to end audio transforms"
    - "audio adversarial examples targeted attacks on speech to text"
    - "learned perceptual image enhancement"
    - "tacotron towards end to end speech synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "learning with learned loss function speech enhancement with quality net to improve perceptual evaluation of speech quality"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "spectral normalization for generative adversarial networks"
    - "deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications"
    - "raw multi channel audio source separation using multi resolution convolutional auto encoders"
    - "supervised speech separation based on deep learning an overview"
    - "segan speech enhancement generative adversarial network"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-5 (induced by source paper "mosnet deep learning based objective assessment for voice conversion"):
  with members:
    - "automos learning a non intrusive assessor of naturalness of speech"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "referenceless performance evaluation of audio source separation using deep neural networks"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "learned perceptual image enhancement"

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)