human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data"):
  Abstract: Sound event detection (SED) aims to detect when and recognize what sound events happen in an audio clip. Many supervised SED algorithms rely on strongly labelled data that contains the onset and offset annotations of sound events. However, many audio tagging datasets are weakly labelled, that is, only the presence of the sound events is known, without knowing their onset and offset annotations. In this paper, we propose a time–frequency (T–F) segmentation framework trained on weakly labelled data to tackle the sound event detection and separation problem. In training, a segmentation mapping is applied on a T–F representation, such as log mel spectrogram of an audio clip to obtain T–F segmentation masks of sound events. The T–F segmentation masks can be used for separating the sound events from the background scenes in the T–F domain. Then, a classification mapping is applied on the T–F segmentation masks to estimate the presence probabilities of the sound events. We model the segmentation mapping using a convolutional neural network and the classification mapping using a global weighted rank pooling. In SED, predicted onset and offset times can be obtained from the T–F segmentation masks. As a byproduct, separated waveforms of sound events can be obtained from the T–F segmentation masks. We remixed the DCASE 2018 Task 1 acoustic scene data with the DCASE 2018 Task 2 sound events data. When mixing under 0 dB, the proposed method achieved F1 scores of 0.534, 0.398, and 0.167 in audio tagging, frame-wise SED and event-wise SED, outperforming the fully connected deep neural network baseline of 0.331, 0.237, and 0.120, respectively. In T–F segmentation, we achieved an F1 score of 0.218, where previous methods were not able to do T–F segmentation.

Includes members:
  Member "general purpose tagging of freesound audio with audioset labels task description dataset and baseline":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi representation knowledge distillation for audio classification")
  Member "dcase 2018 challenge surrey cross task convolutional neural network baseline":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust acoustic scene classification using a multi spectrogram encoder decoder framework")
  Member "dcase 2018 challenge baseline with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cure dataset ladder networks for audio event classification")
  Member "adaptive pooling operators for weakly labeled sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "unsupervised feature learning based on deep models for environmental audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "multiple instance deep learning for weakly supervised small footprint audio event detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep progressive multi scale attention for acoustic event classification")
  Member "a joint separation classification model for sound event detection of weakly labelled data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non negative matrix factorization convolutional neural network nmf cnn for sound event detection")
  Member "deep cnn framework for audio event recognition using weakly labeled web data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep progressive multi scale attention for acoustic event classification")

Question: Please tell me which class the source paper 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)