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 "improving voice separation by incorporating end to end speech recognition"):
  Abstract: Despite recent advances in voice separation methods, many challenges remain in realistic scenarios such as noisy recording and the limits of available data. In this work, we propose to explicitly incorporate the phonetic and linguistic nature of speech by taking a transfer learning approach using an end-to-end automatic speech recognition (E2EASR) system. The voice separation is conditioned on deep features extracted from E2EASR to cover the long-term dependence of phonetic aspects. Experimental results on speech separation and enhancement task on the AVSpeech dataset show that the proposed method significantly improves the signal-to-distortion ratio over the baseline model and even outperforms an audio visual model, that utilizes visual information of lip movements.

Includes members:
  Member "wham extending speech separation to noisy environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "continuous speech separation dataset and analysis")
  Member "recursive speech separation for unknown number of speakers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning extractors for music source separation")
  Member "the conversation deep audio visual speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "audio visual speech enhancement using conditional variational auto encoder")
  Member "espnet end to end speech processing toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "looking to listen at the cocktail party a speaker independent audio visual model for speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "mmdenselstm an efficient combination of convolutional and recurrent neural networks 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 "multi scale multi band densenets for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "aenet learning deep audio features for video analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "autofoley artificial synthesis of synchronized sound tracks for silent videos with deep learning")

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)