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: "end to end learning for music audio tagging at scale"
  Abstract: The lack of data tends to limit the outcomes of deep learning research, particularly when dealing with end-to-end learning stacks processing raw data such as waveforms. In this study, 1.2M tracks annotated with musical labels are available to train our end-to-end models. This large amount of data allows us to unrestrictedly explore two different design paradigms for music auto-tagging: assumption-free models - using waveforms as input with very small convolutional filters; and models that rely on domain knowledge - log-mel spectrograms with a convolutional neural network designed to learn timbral and temporal features. Our work focuses on studying how these two types of deep architectures perform when datasets of variable size are available for training: the MagnaTagATune (25k songs), the Million Song Dataset (240k songs), and a private dataset of 1.2M songs. Our experiments suggest that music domain assumptions are relevant when not enough training data are available, thus showing how waveform-based models outperform spectrogram-based ones in large-scale data scenarios.

Connected through hyperedge HE-1 (induced by source paper "toward interpretable music tagging with self attention"):
  with members:
    - "image transformer"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "compact generalized non local network"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "nsml meet the mlaas platform with a real world case study"
    - "music transformer"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "machine learning for music genre multifaceted review and experimentation with audioset"):
  with members:
    - "end to end speech recognition from the raw waveform"
    - "learning to separate object sounds by watching unlabeled video"
    - "audio based disambiguation of music genre tags"
    - "music mood detection based on audio and lyrics with deep neural net"
    - "the microsoft 2017 conversational speech recognition system"
    - "surrey cvssp system for dcase2017 challenge task 4"
    - "audio set classification with attention model a probabilistic perspective"
    - "reducing model complexity for dnn based large scale audio 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 "panns large scale pretrained audio neural networks for audio pattern recognition"):
  with members:
    - "musicnn pre trained convolutional neural networks for music audio tagging"
    - "bottom up broadcast neural network for music genre classification"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "weakly labelled audioset tagging with attention neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "learning complex basis functions for invariant representations of audio"):
  with members:
    - "gabor convolutional networks"
    - "learning transposition invariant interval features from symbolic music and audio"
    - "audio to score alignment using transposition invariant features"
    - "oriented response networks"
    - "deformable convolutional networks"
    - "transform invariant auto encoder"
    - "harmonic networks deep translation and rotation equivariance"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "nnaudio an on the fly gpu audio to spectrogram conversion toolbox using 1d convolution neural networks"):
  with members:
    - "adversarial learning for improved onsets and frames music transcription"
    - "towards interpretable polyphonic transcription with invertible neural networks"
    - "an attention mechanism for musical instrument recognition"
    - "zero shot learning for audio based music classification and tagging"
    - "learning soft attention models for tempo invariant audio sheet music retrieval"
    - "a bi directional transformer for musical chord recognition"
    - "cover detection using dominant melody embeddings"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"

Connected through hyperedge HE-7 (induced by source paper "music auto tagging using cnns and mel spectrograms with reduced frequency and time resolution"):
  with members:
    - "a tutorial on deep learning for music information retrieval"
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "multi label music genre classification from audio text and images using deep features"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-8 (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"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "feature pyramid networks for object detection"
    - "automatic tagging using deep convolutional neural networks"
    - "learning multiscale features directly from waveforms"
    - "convolutional recurrent neural networks for music classification"

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)