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: "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
  Abstract: Audio tagging aims to perform multi-label classification on audio chunks and it is a newly proposed task in the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE 2016) challenge. This task encourages research efforts to better analyze and understand the content of the huge amounts of audio data on the web. The difficulty in audio tagging is that it only has a chunk-level label without a frame-level label. This paper presents a weakly supervised method to not only predict the tags but also indicate the temporal locations of the occurred acoustic events. The attention scheme is found to be effective in identifying the important frames while ignoring the unrelated frames. The proposed framework is a deep convolutional recurrent model with two auxiliary modules: an attention module and a localization module. The proposed algorithm was evaluated on the Task 4 of DCASE 2016 challenge. State-of-the-art performance was achieved on the evaluation set with equal error rate (EER) reduced from 0.13 to 0.11, compared with the convolutional recurrent baseline system.

Connected through hyperedge HE-1 (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-2 (induced by source paper "deep progressive multi scale attention for acoustic event classification"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "learning environmental sounds with multi scale convolutional neural network"
    - "attention based deep multiple instance learning"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised temporal localization via occurrence count learning"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "onsets and frames dual objective piano transcription"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "a joint detection classification model for audio tagging of weakly labelled data"

Connected through hyperedge HE-4 (induced by source paper "learning discriminative and robust time frequency representations for environmental sound classification"):
  with members:
    - "multi stream network with temporal attention for environmental sound classification"
    - "attention based convolutional recurrent neural network for environmental sound classification"
    - "weakly labelled audioset tagging with attention neural networks"
    - "spatio temporal attention pooling for audio scene classification"
    - "learning environmental sounds with multi scale convolutional neural network"
    - "raw waveform based audio classification using sample level cnn architectures"
    - "learning from between class examples for deep sound recognition"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "improving performance and inference on audio classification tasks using capsule networks"):
  with members:
    - "brain tumor type classification via capsule networks"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "capsule routing for sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "dynamic routing between capsules"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "musan a music speech and noise corpus"

Connected through hyperedge HE-6 (induced by source paper "duration robust sound event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "connectionist temporal localization for sound event detection with sequential labeling"
    - "polyphonic audio tagging with sequentially labelled data using crnn with learnable gated linear units"
    - "capsule routing for sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "language modeling with gated convolutional networks"
    - "a light cnn for deep face representation with noisy labels"

Connected through hyperedge HE-7 (induced by source paper "learning sound events from webly labeled data"):
  with members:
    - "co sampling training robust networks for extremely noisy supervision"
    - "joint optimization framework for learning with noisy labels"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "decoupling when to update from how to update"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "understanding deep learning requires rethinking generalization"
    - "cnn architectures for large scale audio classification"
    - "audio event detection using weakly labeled data"

Connected through hyperedge HE-8 (induced by source paper "sample mixed based data augmentation for domestic audio tagging"):
  with members:
    - "mixup based acoustic scene classification using multi channel convolutional neural network"
    - "shakedrop regularization for deep residual learning"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "mixup beyond empirical risk minimization"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "shake shake regularization"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "automatic tagging using deep convolutional neural 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)