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 "source separation with weakly labelled data an approach to computational auditory scene analysis"):
  Abstract: Source separation is the task of separating an audio recording into individual sound sources. Source separation is fundamental for computational auditory scene analysis. Previous work on source separation has focused on separating particular sound classes such as speech and music. Much previous work requires mixtures and clean source pairs for training. In this work, we propose a source separation framework trained with weakly labelled data. Weakly labelled data only contains#R##N#the tags of an audio clip, without the occurrence time of sound events. We first train a sound event detection system with AudioSet. The trained sound event detection system is used to detect segments that are most likely to contain a target sound event. Then a regression is learnt from a mixture of two randomly selected segments to a target segment conditioned on the audio tagging prediction of the target segment. Our proposed#R##N#system can separate 527 kinds of sound classes from#R##N#AudioSet within a single system. A U-Net is adopted for the separation system and achieves an average SDR of 5.67 dB over 527 sound classes in AudioSet.

Includes members:
  Member "audio source separation using variational autoencoders and weak class supervision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding strength in weakness learning to separate sounds with weak supervision")
  Member "sound event detection and time frequency segmentation from weakly labelled data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non negative matrix factorization convolutional neural network nmf cnn for sound event detection")
  Member "universal sound separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time domain audio source separation based on wave u net combined with discrete wavelet transform")
  Member "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust acoustic scene classification using a multi spectrogram encoder decoder framework")
  Member "panns large scale pretrained audio neural networks for audio pattern recognition":
    also connected through other hyperedges:
      (none)
  Member "unsupervised audio source separation via spectrum energy preserved wasserstein learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding strength in weakness learning to separate sounds with weak supervision")
  Member "multi scale multi band densenets for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "time domain audio source separation based on wave u net combined with discrete wavelet transform")
  Member "adversarial semi supervised audio source separation applied to singing voice extraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "source separation with deep generative priors")

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)