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: "multiclass language identification using deep learning on spectral images of audio signals"
  Abstract: The first step in any voice recognition software is to determine what language a speaker is using, and ideally this process would be automated. The technique described in this paper, language identification for audio spectrograms (LIFAS), uses spectrograms generated from audio signals as inputs to a convolutional neural network (CNN) to be used for language identification. LIFAS requires minimal pre-processing on the audio signals as the spectrograms are generated during each batch as they are input to the network during training. #R##N#LIFAS utilizes deep learning tools that are shown to be successful on image processing tasks and applies it to audio signal classification. LIFAS performs binary language classification with an accuracy of 97\%, and multi-class classification with six languages at an accuracy of 89\% on 3.75 second audio clips.

Connected through hyperedge HE-1 (induced by source paper "fastai a layered api for deep learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "developing an app to interpret chest x rays to support the diagnosis of respiratory pathology with artificial intelligence"
    - "suggestion mining from online reviews using ulmfit"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "mish a self regularized non monotonic neural activation function"
    - "bag of tricks for image classification with convolutional neural networks"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"

Connected through hyperedge HE-2 (induced by source paper "spoken language identification using convnets"):
  with members:
    - "general audio tagging with ensembling convolutional neural networks and statistical features"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "raw waveform based audio classification using sample level cnn architectures"
    - "mixup beyond empirical risk minimization"
    - "language identification using deep convolutional recurrent neural networks"
    - "cnn architectures for large scale audio classification"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "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)