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: "codebook based audio feature representation for music information retrieval"
  Abstract: Digital music has become prolific in the web in recent decades. Automated recommendation systems are essential for users to discover music they love and for artists to reach appropriate audience. When manual annotations and user preference data is lacking (e.g. for new artists) these systems must rely on content based methods. Besides powerful machine learning tools for classification and retrieval, a key component for successful recommendation is the audio content representation. Good representations should capture informative musical patterns in the audio signal of songs. These representations should be concise, to enable efficient (low storage, easy indexing, fast search) management of huge music repositories, and should also be easy and fast to compute, to enable real-time interaction with a user supplying new songs to the system. Before designing new audio features, we explore the usage of traditional local features, while adding a stage of encoding with a pre-computed codebook and a stage of pooling to get compact vectorial representations. We experiment with different encoding methods, namely the LASSO, vector quantization (VQ) and cosine similarity (CS). We evaluate the representations' quality in two music information retrieval applications: query-by-tag and query-by-example. Our results show that concise representations can be used for successful performance in both applications. We recommend using top-τ VQ encoding, which consistently performs well in both applications, and requires much less computation time than the LASSO.

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 "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"
    - "automatic large scale classification of bird sounds is strongly improved by unsupervised feature learning"
    - "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-3 (induced by source paper "representation learning of music using artist labels"):
  with members:
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "content based representations of audio using siamese neural networks"
    - "fma a dataset for music analysis"
    - "automatic tagging using deep convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep learning and music adversaries"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "prediction and characterization of high activity events in social media triggered by real world news"):
  with members:
    - "characterizing the life cycle of online news stories using social media reactions"
    - "exploring text virality in social networks"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

Connected through hyperedge HE-5 (induced by source paper "using generic summarization to improve music information retrieval tasks"):
  with members:
    - "on the application of generic summarization algorithms to music"
    - "mlpack a scalable c machine learning library"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-6 (induced by source paper "early prediction and characterization of high impact world events using social media"):
  with members:
    - "characterizing the life cycle of online news stories using social media reactions"
    - "exploring text virality in social networks"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

Connected through hyperedge HE-7 (induced by source paper "a deep bag of features model for music auto tagging"):
  with members:
    - "representation learning a review and new perspectives"
    - "adadelta an adaptive learning rate method"

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.IR(Information Retrieval)