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: "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
  Abstract: In this work we propose approaches to effectively transfer knowledge from weakly labeled web audio data. We first describe a convolutional neural network (CNN) based framework for sound event detection and classification using weakly labeled audio data. Our model trains efficiently from audios of variable lengths; hence, it is well suited for transfer learning. We then propose methods to learn representations using this model which can be effectively used for solving the target task. We study both transductive and inductive transfer learning tasks, showing the effectiveness of our methods for both domain and task adaptation. We show that the learned representations using the proposed CNN model generalizes well enough to reach human level accuracy on ESC-50 sound events dataset and set state of art results on this dataset. We further use them for acoustic scene classification task and once again show that our proposed approaches suit well for this task as well. We also show that our methods are helpful in capturing semantic meanings and relations as well. Moreover, in this process we also set state-of-art results on Audioset dataset, relying on balanced training set.

Connected through hyperedge HE-1 (induced by source paper "cure dataset ladder networks for audio event classification"):
  with members:
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "audio event detection using weakly labeled data"
    - "soundnet learning sound representations from unlabeled video"
    - "deconstructing the ladder network architecture"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-2 (induced by source paper "coordinated joint multimodal embeddings for generalized audio visual zeroshot classification and retrieval of videos"):
  with members:
    - "zero shot detection"
    - "learning to localize sound source in visual scenes"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "identify locate and separate audio visual object extraction in large video collections using weak supervision"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"

Connected through hyperedge HE-3 (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-4 (induced by source paper "sirenattack generating adversarial audio for end to end acoustic systems"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "continuous authentication for voice assistants"
    - "dolphinattack inaudible voice commands"
    - "the insecurity of home digital voice assistants amazon alexa as a case study"
    - "dual path networks"
    - "exploring neural transducers for end to end speech recognition"
    - "crafting adversarial examples for speech paralinguistics applications"

Connected through hyperedge HE-5 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"):
  with members:
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "a novel orthogonal direction mesh adaptive direct search approach for svm hyperparameter tuning"
    - "randomly weighted cnns for music audio classification"
    - "environmental sound classification based on multi temporal resolution convolutional neural network combining with multi level features"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-6 (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"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"

Connected through hyperedge HE-7 (induced by source paper "guided learning for weakly labeled semi supervised sound event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (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"
    - "a joint detection classification model for audio tagging of weakly labelled data"

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)