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: "learning sound event classifiers from web audio with noisy labels"
  Abstract: As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little research into the impact of these errors. To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42.5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data. We characterize the label noise empirically, and provide a CNN baseline system. Experiments suggest that training with large amounts of noisy data can outperform training with smaller amounts of carefully-labeled data. We also show that noise-robust loss functions can be effective in improving performance in presence of corrupted labels.

Connected through hyperedge HE-1 (induced by source paper "learning with out of distribution data for audio classification"):
  with members:
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mixup as locally linear out of manifold regularization"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "mixup beyond empirical risk minimization"
    - "learning from noisy labels with distillation"
    - "densely connected convolutional networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "urban sound tagging using convolutional neural networks"):
  with members:
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "attention based convolutional recurrent neural network for environmental sound classification"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "random erasing data augmentation"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "model agnostic approaches to handling noisy labels when training sound event classifiers"):
  with members:
    - "audio tagging with noisy labels and minimal supervision"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "a simple fusion of deep and shallow learning for acoustic scene classification"
    - "learning sound events from webly labeled data"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-4 (induced by source paper "audio tagging with noisy labels and minimal supervision"):
  with members:
    - "model agnostic approaches to handling noisy labels when training sound event classifiers"
    - "do imagenet classifiers generalize to imagenet"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "facilitating the manual annotation of sounds when using large taxonomies"
    - "learning sound events from webly labeled data"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "secost sequential co supervision for weakly labeled audio event detection"):
  with members:
    - "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"
    - "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-6 (induced by source paper "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "audio tagging with noisy labels and minimal supervision"
    - "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "a hybrid parametric deep learning approach for sound event localization and detection"):
  with members:
    - "model agnostic approaches to handling noisy labels when training sound event classifiers"
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "a simple fusion of deep and shallow learning for acoustic scene classification"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mixup beyond empirical risk minimization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "supervised classifiers for audio impairments with noisy labels"):
  with members:
    - "deep learning is robust to massive label noise"
    - "a closer look at weak label learning for audio events"
    - "joint optimization framework for learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "learning deep networks from noisy labels with dropout regularization"
    - "training deep neural networks on noisy labels with bootstrapping"

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)