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: "adaptive pooling operators for weakly labeled sound event detection"
  Abstract: Sound event detection (SED) methods are tasked with labeling segments of audio recordings by the presence of active sound sources. SED is typically posed as a supervised machine learning problem, requiring strong annotations for the presence or absence of each sound source at every time instant within the recording. However, strong annotations of this type are both labor- and cost-intensive for human annotators to produce, which limits the practical scalability of SED methods. #R##N#In this work, we treat SED as a multiple instance learning (MIL) problem, where training labels are static over a short excerpt, indicating the presence or absence of sound sources but not their temporal locality. The models, however, must still produce temporally dynamic predictions, which must be aggregated (pooled) when comparing against static labels during training. To facilitate this aggregation, we develop a family of adaptive pooling operators---referred to as auto-pool---which smoothly interpolate between common pooling operators, such as min-, max-, or average-pooling, and automatically adapt to the characteristics of the sound sources in question. We evaluate the proposed pooling operators on three datasets, and demonstrate that in each case, the proposed methods outperform non-adaptive pooling operators for static prediction, and nearly match the performance of models trained with strong, dynamic annotations. The proposed method is evaluated in conjunction with convolutional neural networks, but can be readily applied to any differentiable model for time-series label prediction.

Connected through hyperedge HE-1 (induced by source paper "limitations of weak labels for embedding and tagging"):
  with members:
    - "learning problem agnostic speech representations from multiple self supervised tasks"
    - "massively parallel hyperparameter tuning"
    - "a closer look at weak label learning for audio events"
    - "training neural audio classifiers with few data"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "prototypical networks for few shot learning"
    - "learning from between class examples for deep sound recognition"
    - "learning from weak and noisy labels for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "non negative matrix factorization convolutional neural network nmf cnn for sound event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "hodgepodge sound event detection based on ensemble of semi supervised learning methods"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "pcanet a simple deep learning baseline for image classification"

Connected through hyperedge HE-3 (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"
    - "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"
    - "audio set classification with attention model a probabilistic perspective"

Connected through hyperedge HE-4 (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"
    - "weakly labelled audioset tagging with attention neural networks"
    - "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"
    - "convolutional recurrent neural networks for polyphonic sound event detection"

Connected through hyperedge HE-5 (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-6 (induced by source paper "an attention mechanism for musical instrument recognition"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "frame level instrument recognition by timbre and pitch"
    - "multitask learning for frame level instrument recognition"
    - "deep sets"
    - "extended playing techniques the next milestone in musical instrument recognition"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "audio caption listen and tell"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "end to end contextual speech recognition using class language models and a token passing decoder"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "automated audio captioning with recurrent neural networks"
    - "weakly supervised dense video captioning"
    - "multi task video captioning with video and entailment generation"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-8 (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"
    - "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"

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)