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: "audio spectrogram representations for processing with convolutional neural networks"
  Abstract: One of the decisions that arise when designing a neural network for any application is how the data should be represented in order to be presented to, and possibly generated by, a neural network. For audio, the choice is less obvious than it seems to be for visual images, and a variety of representations have been used for different applications including the raw digitized sample stream, hand-crafted features, machine discovered features, MFCCs and variants that include deltas, and a variety of spectral representations. This paper reviews some of these representations and issues that arise, focusing particularly on spectrograms for generating audio using neural networks for style transfer.

Connected through hyperedge HE-1 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges"):
  with members:
    - "audio style transfer"
    - "neural style transfer for audio spectograms"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "neural style transfer a review"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-2 (induced by source paper "improving automated segmentation of radio shows with audio embeddings"):
  with members:
    - "sound word2vec learning word representations grounded in sounds"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "soundnet learning sound representations from unlabeled video"
    - "very deep convolutional networks for large scale image recognition"
    - "advances in domain independent linear text segmentation"

Connected through hyperedge HE-3 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"):
  with members:
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "a novel orthogonal direction mesh adaptive direct search approach for svm hyperparameter tuning"
    - "randomly weighted cnns for music audio classification"
    - "environmental sound classification based on multi temporal resolution convolutional neural network combining with multi level features"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "urban sound tagging using convolutional neural networks"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "attention based convolutional recurrent neural network for environmental sound classification"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "random erasing data augmentation"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "data augmentation approaches for improving animal audio classification"):
  with members:
    - "deep learning a critical appraisal"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "aenet learning deep audio features for video analysis"
    - "multi label music genre classification from audio text and images using deep features"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "places an image database for deep scene understanding"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "towards robust voice pathology detection"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "densely connected convolutional networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a lightweight music texture transfer system"):
  with members:
    - "a universal music translation network"
    - "toward controlled generation of text"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "style transfer in text exploration and evaluation"
    - "time domain neural audio style transfer"
    - "shakespearizing modern language using copy enriched sequence to sequence models"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-8 (induced by source paper "audio spectrogram factorization for classification of telephony signals below the auditory threshold"):
  with members:
    - "riddim a rhythm analysis and decomposition tool based on independent subspace analysis"
    - "semi supervised model training for unbounded conversational speech recognition"
    - "a neural network alternative to non negative audio models"

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)