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: "automatic large scale classification of bird sounds is strongly improved by unsupervised feature learning"
  Abstract: Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to improve its accuracy while ensuring that it can run at big data scales. Many approaches use acoustic measures based on spectrogram-type data, such as the Mel-frequency cepstral coefficient (MFCC) features which represent a manually-designed summary of spectral information. However, recent work in machine learning has demonstrated that features learnt automatically from data can often outperform manually-designed feature transforms. Feature learning can be performed at large scale and “unsupervised”, meaning it requires no manual data labelling, yet it can improve performance on “supervised” tasks such as classification. In this work we introduce a technique for feature learning from large volumes of bird sound recordings, inspired by techniques that have proven useful in other domains. We experimentally compare twelve different feature representations derived from the Mel spectrum (of which six use this technique), using four large and diverse databases of bird vocalisations, classified using a random forest classifier. We demonstrate that in our classification tasks, MFCCs can often lead to worse performance than the raw Mel spectral data from which they are derived. Conversely, we demonstrate that unsupervised feature learning provides a substantial boost over MFCCs and Mel spectra without adding computational complexity after the model has been trained. The boost is particularly notable for single-label classification tasks at large scale. The spectro-temporal activations learned through our procedure resemble spectro-temporal receptive fields calculated from avian primary auditory forebrain. However, for one of our datasets, which contains substantial audio data but few annotations, increased performance is not discernible. We study the interaction between dataset characteristics and choice of feature representation through further empirical analysis.

Connected through hyperedge HE-1 (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"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-2 (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-3 (induced by source paper "cross representation transferability of adversarial perturbations from spectrograms to audio waveforms"):
  with members:
    - "universal adversarial audio perturbations"
    - "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "randomly weighted cnns for music audio classification"
    - "multi label music genre classification from audio text and images using deep features"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"

Connected through hyperedge HE-4 (induced by source paper "data efficient classification of birdcall through convolutional neural networks transfer learning"):
  with members:
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "audio recording device identification based on deep learning"
    - "multi view face detection using deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "automatic rain and cicada chorus filtering of bird acoustic data"):
  with members:
    - "bird detection in audio a survey and a challenge"
    - "estimating continuous distributions in bayesian classifiers"
    - "the orchive data mining a massive bioacoustic archive"

Connected through hyperedge HE-6 (induced by source paper "stacked convolutional and recurrent neural networks for bird audio detection"):
  with members:
    - "convolutional recurrent neural networks for bird audio detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "bird detection in audio a survey and a challenge"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "theano a python framework for fast computation of mathematical expressions"
    - "convolutional recurrent neural networks for music classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "unsupervised feature learning based on deep models for environmental audio tagging"):
  with members:
    - "audio event detection using weakly labeled data"
    - "youtube 8m a large scale video classification benchmark"
    - "automatic tagging using deep convolutional neural networks"
    - "yfcc100m the new data in multimedia research"
    - "codebook based audio feature representation for music information retrieval"
    - "fully convolutional networks for semantic segmentation"
    - "representation learning a review and new perspectives"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-8 (induced by source paper "automatic environmental sound recognition performance versus computational cost"):
  with members:
    - "deep learning with limited numerical precision"
    - "going deeper with convolutions"
    - "speech recognition with deep recurrent neural networks"
    - "internet of things iot a vision architectural elements and future directions"
    - "adadelta an adaptive learning rate method"
    - "advances in optimizing recurrent networks"

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)