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: "weakly labelled audioset tagging with attention neural networks"
  Abstract: Audio tagging is the task of predicting the presence or absence of sound classes within an audio clip. Previous work in audio tagging focused on relatively small datasets limited to recognizing a small number of sound classes. We investigate audio tagging on AudioSet, which is a dataset consisting of over 2 million audio clips and 527 classes. AudioSet is weakly labelled, in that only the presence or absence of sound classes is known for each clip, whereas the onset and offset times are unknown. To address the weakly labelled audio tagging problem, we propose attention neural networks as a way to attend the most salient parts of an audio clip. We bridge the connection between attention neural networks and multiple instance learning (MIL) methods, and propose decision-level and feature-level attention neural networks for audio tagging. We investigate attention neural networks modeled by different functions, depths, and widths. Experiments on AudioSet show that the feature-level attention neural network achieves a state-of-the-art mean average precision of 0.369, outperforming the best MIL method of 0.317 and Google's deep neural network baseline of 0.314. In addition, we discover that the audio tagging performance on AudioSet-embedding features has a weak correlation with the number of training samples and the quality of labels of each sound class.

Connected through hyperedge HE-1 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection"):
  with members:
    - "sound event detection using graph laplacian regularization based on event co occurrence"
    - "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "attention based deep multiple instance learning"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"

Connected through hyperedge HE-2 (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"
    - "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"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "panns large scale pretrained audio neural networks for audio pattern recognition"):
  with members:
    - "musicnn pre trained convolutional neural networks for music audio tagging"
    - "bottom up broadcast neural network for music genre classification"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "audio set classification with attention model a probabilistic perspective"

Connected through hyperedge HE-4 (induced by source paper "secost sequential co supervision for weakly labeled audio event detection"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "continual lifelong learning with neural networks a review"
    - "learning sound events from webly labeled data"
    - "a closer look at weak label learning for audio events"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"

Connected through hyperedge HE-5 (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"
    - "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"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "mixup beyond empirical risk minimization"

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)