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: "the state of the art ten years after a state of the art future research in music information retrieval"
  Abstract: AbstractA decade has passed since the first review of research on a ‘flagship application’ of music information retrieval (MIR): the problem of music genre recognition (MGR). During this time, about 500 works addressing MGR have been published, and at least 10 campaigns have been run to evaluate MGR systems. This makes MGR one of the most researched areas of MIR. So, where does MGR now lie? We show that in spite of this massive amount of work, MGR does not lie far from where it began, and the paramount reason for this is that most evaluation in MGR lacks validity. We perform a case study of all published research using the most-used benchmark dataset in MGR during the past decade: GTZAN. We show that none of the evaluations in these many works is valid to produce conclusions with respect to recognizing genre, i.e. that a system is using criteria relevant for recognizing genre. In fact, the problems of validity in evaluation also affect research in music emotion recognition and autotagging. We conclude by ...

Connected through hyperedge HE-1 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis"):
  with members:
    - "predicting time to failure of plasma etching equipment using machine learning"
    - "cross modal music retrieval and applications an overview of key methodologies"
    - "multi task music representation learning from multi label embeddings"
    - "fashion and apparel classification using convolutional neural networks"
    - "a multi modal deep neural network approach to bird song identification"
    - "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "content based video music retrieval using soft intra modal structure constraint"

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 "random projections of mel spectrograms as low level features for automatic music genre classification"):
  with members:
    - "multi label music genre classification from audio text and images using deep features"
    - "transfer learning for music classification and regression tasks"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-4 (induced by source paper "texture selection for automatic music genre classification"):
  with members:
    - "music genre classification using spectral analysis and sparse representation of the signals"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "transfer learning for music classification and regression tasks"

Connected through hyperedge HE-5 (induced by source paper "a music classification model based on metric learning and feature extraction from mp3 audio files"):
  with members:
    - "music data analysis a state of the art survey"

Connected through hyperedge HE-6 (induced by source paper "one deep music representation to rule them all a comparative analysis of different representation learning strategies"):
  with members:
    - "a comparison on audio signal preprocessing methods for deep neural networks on music tagging"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "look listen and learn"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "transfer learning for music classification and regression tasks"
    - "one model to learn them all"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "fma a dataset for music analysis"

Connected through hyperedge HE-7 (induced by source paper "deep layered learning in mir"):
  with members:
    - "polyphonic pitch tracking with deep layered learning"
    - "onsets and frames dual objective piano transcription"
    - "towards playlist generation algorithms using rnns trained on within track transitions"
    - "deep residual learning for image recognition"
    - "learning fine grained features via a cnn tree for large scale classification"
    - "the application of two level attention models in deep convolutional neural network for fine grained image classification"
    - "attention for fine grained categorization"
    - "knowledge matters importance of prior information for optimization"

Connected through hyperedge HE-8 (induced by source paper "on large scale genre classification in symbolically encoded music by automatic identification of repeating patterns"):
  with members:
    - "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.SD(Sound)