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: "a closer look at weak label learning for audio events"
  Abstract: Audio content analysis in terms of sound events is an important research problem for a variety of applications. Recently, the development of weak labeling approaches for audio or sound event detection (AED) and availability of large scale weakly labeled dataset have finally opened up the possibility of large scale AED. However, a deeper understanding of how weak labels affect the learning for sound events is still missing from literature. In this work, we first describe a CNN based approach for weakly supervised training of audio events. The approach follows some basic design principle desirable in a learning method relying on weakly labeled audio. We then describe important characteristics, which naturally arise in weakly supervised learning of sound events. We show how these aspects of weak labels affect the generalization of models. More specifically, we study how characteristics such as label density and corruption of labels affects weakly supervised training for audio events. We also study the feasibility of directly obtaining weak labeled data from the web without any manual label and compare it with a dataset which has been manually labeled. The analysis and understanding of these factors should be taken into picture in the development of future weak label learning methods. Audioset, a large scale weakly labeled dataset for sound events is used in our experiments.

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"
    - "training neural audio classifiers with few data"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "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 "learning sound event classifiers from web audio with noisy labels"):
  with members:
    - "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"
    - "joint optimization framework for learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "iterative learning with open set noisy labels"
    - "data efficient weakly supervised learning for low resource audio event detection using deep learning"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "learning from noisy large scale datasets with minimal supervision"

Connected through hyperedge HE-3 (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 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-4 (induced by source paper "supervised classifiers for audio impairments with noisy labels"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "deep learning is robust to massive label noise"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "activity recognition on a large scale in short videos moments in time dataset"):
  with members:
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "temporal relational reasoning in videos"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "cuhk ethz siat submission to activitynet challenge 2016"
    - "review of action recognition and detection methods"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-6 (induced by source paper "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"):
  with members:
    - "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"
    - "audio set classification with attention model a probabilistic perspective"
    - "reducing model complexity for dnn based large scale audio classification"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "aclnet efficient end to end audio classification cnn"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mixup beyond empirical risk minimization"
    - "very deep convolutional neural networks for raw waveforms"
    - "soundnet learning sound representations from unlabeled video"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "very deep convolutional networks for large scale image recognition"

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)