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: "multi label music genre classification from audio text and images using deep features"
  Abstract: Music genres allow to categorize musical items that share common characteristics. Although these categories are not mutually exclusive, most related research is traditionally focused on classifying tracks into a single class. Furthermore, these categories (e.g., Pop, Rock) tend to be too broad for certain applications. In this work we aim to expand this task by categorizing musical items into multiple and fine-grained labels, using three different data modalities: audio, text, and images. To this end we present MuMu, a new dataset of more than 31k albums classified into 250 genre classes. For every album we have collected the cover image, text reviews, and audio tracks. Additionally, we propose an approach for multi-label genre classification based on the combination of feature embeddings learned with state-of-the-art deep learning methodologies. Experiments show major differences between modalities, which not only introduce new baselines for multi-label genre classification, but also suggest that combining them yields improved results.

Connected through hyperedge HE-1 (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-2 (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 level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-3 (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"
    - "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"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-4 (induced by source paper "data augmentation approaches for improving animal audio classification"):
  with members:
    - "deep learning a critical appraisal"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "aenet learning deep audio features for video analysis"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "places an image database for deep scene understanding"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "labelsens enabling real time sensor data labelling at the point of collection on edge computing"):
  with members:
    - "challenges of designing and developing tangible interfaces for mental well being"
    - "notimind utilizing responses to smart phone notifications as affective sensors"
    - "comparative study of cnn and rnn for natural language processing"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "neural gpus learn algorithms"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "random projections of mel spectrograms as low level features for automatic music genre classification"):
  with members:
    - "transfer learning for music classification and regression tasks"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"
    - "the state of the art ten years after a state of the art future research in music information retrieval"

Connected through hyperedge HE-7 (induced by source paper "leveraging knowledge bases and parallel annotations for music genre translation"):
  with members:
    - "audio based disambiguation of music genre tags"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "adam a method for stochastic optimization"

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"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"

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)