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: "spectral feature mapping with mimic loss for robust speech recognition"
  Abstract: For the task of speech enhancement, local learning objectives are agnostic to phonetic structures helpful for speech recognition. We propose to add a global criterion to ensure de-noised speech is useful for downstream tasks like ASR. We first train a spectral classifier on clean speech to predict senone labels. Then, the spectral classifier is joined with our speech enhancer as a noisy speech recognizer. This model is taught to imitate the output of the spectral classifier alone on clean speech. This \textit{mimic loss} is combined with the traditional local criterion to train the speech enhancer to produce de-noised speech. Feeding the de-noised speech to an off-the-shelf Kaldi training recipe for the CHiME-2 corpus shows significant improvements in WER.

Connected through hyperedge HE-1 (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 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-2 (induced by source paper "an exploration of mimic architectures for residual network based spectral mapping"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "very deep convolutional networks for end to end speech recognition"
    - "a fully convolutional neural network for speech enhancement"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "very deep convolutional neural networks for robust speech recognition"
    - "the microsoft 2016 conversational speech recognition system"
    - "wide residual networks"
    - "deep residual learning for image recognition"

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)