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: "learning multiscale features directly from waveforms"
  Abstract: Deep learning has dramatically improved the performance of speech recognition systems through learning hierarchies of features optimized for the task at hand. However, true end-to-end learning, where features are learned directly from waveforms, has only recently reached the performance of hand-tailored representations based on the Fourier transform. In this paper, we detail an approach to use convolutional filters to push past the inherent tradeoff of temporal and frequency resolution that exists for spectral representations. At increased computational cost, we show that increasing temporal resolution via reduced stride and increasing frequency resolution via additional filters delivers significant performance improvements. Further, we find more efficient representations by simultaneously learning at multiple scales, leading to an overall decrease in word error rate on a difficult internal speech test set by 20.7% relative to networks with the same number of parameters trained on spectrograms.

Connected through hyperedge HE-1 (induced by source paper "gansynth adversarial neural audio synthesis"):
  with members:
    - "augmented cyclic adversarial learning for low resource domain adaptation"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "sing symbol to instrument neural generator"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "adversarial audio synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"

Connected through hyperedge HE-2 (induced by source paper "deep progressive multi scale attention for acoustic event classification"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "learning environmental sounds with multi scale convolutional neural network"
    - "attention based deep multiple instance learning"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-3 (induced by source paper "adversarial generation of time frequency features with application in audio synthesis"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "a context encoder for audio inpainting"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "large scale gan training for high fidelity natural image synthesis"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "adversarial audio synthesis"
    - "improving dnn based music source separation using phase features"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "neuralogram a deep neural network based representation for audio signals"):
  with members:
    - "audio linguistic embeddings for spoken sentences"
    - "neural style transfer for audio spectograms"
    - "conditional end to end audio transforms"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "voxceleb2 deep speaker recognition"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-5 (induced by source paper "j net randomly weighted u net for audio source separation"):
  with members:
    - "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"
    - "progressive image deraining networks a better and simpler baseline"
    - "convolutional recurrent neural network based progressive learning for monaural speech enhancement"
    - "audio denoising with deep network priors"
    - "randomly weighted cnns for music audio classification"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "deep image prior"

Connected through hyperedge HE-6 (induced by source paper "end to end environmental sound classification using a 1d convolutional neural network"):
  with members:
    - "randomly weighted cnns for music audio classification"
    - "end to end speech recognition from the raw waveform"
    - "sample level cnn architectures for music auto tagging using raw waveforms"
    - "learning from between class examples for deep sound recognition"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "very deep convolutional neural networks for raw waveforms"
    - "cnn architectures for large scale audio classification"
    - "automatic environmental sound recognition performance versus computational cost"

Connected through hyperedge HE-7 (induced by source paper "multi scale embedded cnn for music tagging mse cnn"):
  with members:
    - "panoptic feature pyramid networks"
    - "feature pyramid network for multi class land segmentation"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "end to end learning for music audio tagging at scale"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "feature pyramid networks for object detection"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-8 (induced by source paper "randomly weighted cnns for music audio classification"):
  with members:
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "deep image prior"
    - "dlr toward a deep learned rhythmic representation for music content analysis"
    - "look listen and learn"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "multi label music genre classification from audio text and images using deep features"

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.CL(Computation and Language)