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 tagging using deep convolutional neural networks"
  Abstract: We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs). We evaluate different architectures consisting of 2D convolutional layers and subsampling layers only. In the experiments, we measure the AUC-ROC scores of the architectures with different complexities and input types using the MagnaTagATune dataset, where a 4-layer architecture shows state-of-the-art performance with mel-spectrogram input. Furthermore, we evaluated the performances of the architectures with varying the number of layers on a larger dataset (Million Song Dataset), and found that deeper models outperformed the 4-layer architecture. The experiments show that mel-spectrogram is an effective time-frequency representation for automatic tagging and that more complex models benefit from more training data.

Connected through hyperedge HE-1 (induced by source paper "deep learning for audio signal processing"):
  with members:
    - "piano genie"
    - "monaural speech enhancement using deep neural networks by maximizing a short time objective intelligibility measure"
    - "toward domain invariant speech recognition via large scale training"
    - "efficient neural audio synthesis"
    - "deep contextualized word representations"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "deep cnn framework for audio event recognition using weakly labeled web data"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "sirenattack generating adversarial audio for end to end acoustic systems"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "continuous authentication for voice assistants"
    - "dolphinattack inaudible voice commands"
    - "the insecurity of home digital voice assistants amazon alexa as a case study"
    - "dual path networks"
    - "exploring neural transducers for end to end speech recognition"
    - "crafting adversarial examples for speech paralinguistics applications"

Connected through hyperedge HE-6 (induced by source paper "exploiting synchronized lyrics and vocal features for music emotion detection"):
  with members:
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "end to end music source separation is it possible in the waveform domain"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "music mood detection based on audio and lyrics with deep neural net"
    - "a tutorial on deep learning for music information retrieval"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "weakly labelled audioset tagging with attention neural networks"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "attention based deep multiple instance learning"
    - "identify locate and separate audio visual object extraction in large video collections using weak supervision"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "audio set classification with attention model a probabilistic perspective"
    - "a structured self attentive sentence embedding"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "a joint detection classification model for audio tagging of weakly labelled data"

Connected through hyperedge HE-8 (induced by source paper "sound event detection of weakly labelled data with cnn transformer and automatic threshold optimization"):
  with members:
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "weakly labelled audioset tagging with attention neural networks"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention is all you need"
    - "sound event detection using spatial features and convolutional recurrent neural network"

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)