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: "deep cnn framework for audio event recognition using weakly labeled web data"
  Abstract: The development of audio event recognition models requires labeled training data, which are generally hard to obtain. One promising source of recordings of audio events is the large amount of multimedia data on the web. In particular, if the audio content analysis must itself be performed on web audio, it is important to train the recognizers themselves from such data. Training from these web data, however, poses several challenges, the most important being the availability of labels : labels, if any, that may be obtained for the data are generally {\em weak}, and not of the kind conventionally required for training detectors or classifiers. We propose that learning algorithms that can exploit weak labels offer an effective method to learn from web data. We then propose a robust and efficient deep convolutional neural network (CNN) based framework to learn audio event recognizers from weakly labeled data. The proposed method can train from and analyze recordings of variable length in an efficient manner and outperforms a network trained with {\em strongly labeled} web data by a considerable margin.

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"
    - "audio set classification with attention model a probabilistic perspective"

Connected through hyperedge HE-2 (induced by source paper "deep progressive multi scale attention for acoustic event classification"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "learning environmental sounds with multi scale convolutional neural network"
    - "attention based deep multiple instance learning"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"

Connected through hyperedge HE-3 (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"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "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-4 (induced by source paper "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "r crnn region based convolutional recurrent neural network for audio event detection"
    - "enabling early audio event detection with neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "dnn and cnn with weighted and multi task loss functions for audio event detection"
    - "what makes audio event detection harder than classification"
    - "car forest joint classification regression decision forests for overlapping audio event detection"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"

Connected through hyperedge HE-5 (induced by source paper "enabling early audio event detection with neural networks"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "dnn and cnn with weighted and multi task loss functions for audio event detection"
    - "what makes audio event detection harder than classification"
    - "an overview of multi task learning in deep neural networks"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "car forest joint classification regression decision forests for overlapping audio event detection"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"

Connected through hyperedge HE-6 (induced by source paper "data efficient weakly supervised learning for low resource audio event detection using deep learning"):
  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"
    - "densely connected cnns for bird audio detection"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "audio event detection using weakly labeled data"

Connected through hyperedge HE-7 (induced by source paper "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "audio event detection using weakly labeled data"
    - "theano a python framework for fast computation of mathematical expressions"
    - "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 "dnn and cnn with weighted and multi task loss functions for audio event detection"):
  with members:
    - "an overview of multi task learning in deep neural networks"
    - "learning compact structural representations for audio events using regressor banks"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "car forest joint classification regression decision forests for overlapping audio event detection"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "adam a method for stochastic optimization"

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)