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: "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
  Abstract: This paper proposes a neural network architecture and training scheme to learn the start and end time of sound events (strong labels) in an audio recording given just the list of sound events existing in the audio without time information (weak labels). We achieve this by using a stacked convolutional and recurrent neural network with two prediction layers in sequence one for the strong followed by the weak label. The network is trained using frame-wise log mel-band energy as the input audio feature, and weak labels provided in the dataset as labels for the weak label prediction layer. Strong labels are generated by replicating the weak labels as many number of times as the frames in the input audio feature, and used for strong label layer during training. We propose to control what the network learns from the weak and strong labels by different weighting for the loss computed in the two prediction layers. The proposed method is evaluated on a publicly available dataset of 155 hours with 17 sound event classes. The method achieves the best error rate of 0.84 for strong labels and F-score of 43.3% for weak labels on the unseen test split.

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 "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"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "weakly supervised action localization by sparse temporal pooling 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-3 (induced by source paper "finding strength in weakness learning to separate sounds with weak supervision"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "audio source separation using variational autoencoders and weak class supervision"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "cutting music source separation some slakh a dataset to study the impact of training data quality and quantity"
    - "universal sound separation"
    - "co separating sounds of visual objects"
    - "sdr half baked or well done"

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"
    - "weakly labelled audioset tagging with attention neural networks"
    - "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"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"

Connected through hyperedge HE-5 (induced by source paper "the mbpep a deep ensemble pruning algorithm providing high quality uncertainty prediction"):
  with members:
    - "a hardware friendly unsupervised memristive neural network with weight sharing mechanism"
    - "adam a method for stochastic optimization"
    - "on meta learning for dynamic ensemble selection"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "adaptive pooling operators for weakly labeled sound event detection"):
  with members:
    - "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "look listen and learn"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "a joint detection classification model for audio tagging of weakly labelled data"

Connected through hyperedge HE-7 (induced by source paper "deep learning for audio transcription on low resource datasets"):
  with members:
    - "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "end to end learning for music audio tagging at scale"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "cross stitch networks for multi task learning"
    - "trace norm regularised deep multi task learning"

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"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "mixup beyond empirical risk minimization"
    - "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)