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 "model based stft phase recovery for audio source separation"):
  Abstract: For audio source separation applications, it is common to estimate the magnitude of the short-time Fourier transform (STFT) of each source. In order to further synthesizing time-domain signals, it is necessary to recover the phase of the corresponding complex-valued STFT. Most authors in this field choose a Wiener-like filtering approach which boils down to using the phase of the original mixture. In this paper, a different standpoint is adopted. Many music events are partially composed of slowly varying sinusoids and the STFT phase increment over time of those frequency components takes a specific form. This allows phase recovery by an unwrapping technique once a short-term frequency estimate has been obtained. Herein, a novel iterative source separation procedure is proposed which builds upon these results. It consists in minimizing the mixing error by means of the auxiliary function method. This procedure is initialized by exploiting the unwrapping technique in order to generate estimates that benefit from a temporal continuity property. Experiments conducted on realistic music pieces show that, given accurate magnitude estimates, this procedure outperforms the state-of-the-art consistent Wiener filter.

Includes members:
  Member "multi scale multi band densenets for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "phase reconstruction of spectrograms with linear unwrapping application to audio signal restoration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complex isnmf a phase aware model for monaural audio source separation")
  Member "single channel multi speaker separation using deep clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "la furca iterative context aware end to end monaural speech separation based on dual path deep parallel inter intra bi lstm with attention")
  Member "complex nmf under phase constraints based on signal modeling application to audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "complex isnmf a phase aware model for monaural audio source separation")
  Member "phase reconstruction of spectrograms based on a model of repeated audio events":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "phase recovery by unwrapping applications to music signal processing")
  Member "phase recovery in nmf for audio source separation an insightful benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online spectrogram inversion for low latency audio source separation")
  Member "informed source separation using iterative reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "end to end speech separation with unfolded iterative phase reconstruction")
  Member "wirtinger s calculus in general hilbert spaces":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "phase recovery by unwrapping applications to music signal processing")

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)