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 joint detection classification model for audio tagging of weakly labelled data"
  Abstract: Audio tagging aims to assign one or several tags to an audio clip. Most of the datasets are weakly labelled, which means only the tags of the clip are known, without knowing the occurrence time of the tags. The labeling of an audio clip is often based on the audio events in the clip and no event level label is provided to the user. Previous works have used the bag of frames model assume the tags occur all the time, which is not the case in practice. We propose a joint detection-classification (JDC) model to detect and classify the audio clip simultaneously. The JDC model has the ability to attend to informative and ignore uninformative sounds. Then only informative regions are used for classification. Experimental results on the "CHiME Home" dataset show that the JDC model reduces the equal error rate (EER) from 19.0% to 16.9%. More interestingly, the audio event detector is trained successfully without needing the event level label.

Connected through hyperedge HE-1 (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"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "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"

Connected through hyperedge HE-2 (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"
    - "adaptive pooling operators for weakly labeled sound event detection"

Connected through hyperedge HE-3 (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"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural 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"

Connected through hyperedge HE-4 (induced by source paper "weakly labelled audioset tagging with attention neural networks"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "attention based deep multiple instance learning"
    - "identify locate and separate audio visual object extraction in large video collections using weak supervision"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "audio set classification with attention model a probabilistic perspective"
    - "a structured self attentive sentence embedding"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-5 (induced by source paper "enhancing sound texture in cnn based acoustic scene classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "reducing model complexity for dnn based large scale audio classification"
    - "cnn architectures for large scale audio classification"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "acoustic scene classification classifying environments from the sounds they produce"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

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"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "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 "weakly supervised crnn system for sound event detection with large scale unlabeled in domain data"):
  with members:
    - "general audio tagging with ensembling convolutional neural networks and statistical features"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "deep residual learning for 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)