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: "phase recovery in nmf for audio source separation an insightful benchmark"
  Abstract: Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing mixtures of audio signals in the Time-Frequency (TF) domain. In applications such as source separation, the phase recovery for each extracted component is a major issue since it often leads to audible artifacts. In this paper, we present a methodology for evaluating various NMF-based source separation techniques involving phase reconstruction. For each model considered, a comparison between two approaches (blind separation without prior information and oracle separation with supervised model learning) is performed, in order to inquire about the room for improvement for the estimation methods. Experimental results show that the High Resolution NMF (HRNMF) model is particularly promising, because it is able to take phases and correlations over time into account with a great expressive power.

Connected through hyperedge HE-1 (induced by source paper "online spectrogram inversion for low latency audio source separation"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "low latency deep clustering for speech separation"
    - "sdr half baked or well done"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "deep learning based phase reconstruction for speaker separation a trigonometric perspective"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "differentiable consistency constraints for improved deep speech enhancement"
    - "supervised speech separation based on deep learning an overview"

Connected through hyperedge HE-2 (induced by source paper "complex isnmf a phase aware model for monaural audio source separation"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "phase reconstruction of spectrograms with linear unwrapping application to audio signal restoration"
    - "complex nmf under phase constraints based on signal modeling application to audio source separation"
    - "model based stft phase recovery for audio source separation"

Connected through hyperedge HE-3 (induced by source paper "model based stft phase recovery for audio source separation"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "phase reconstruction of spectrograms with linear unwrapping application to audio signal restoration"
    - "single channel multi speaker separation using deep clustering"
    - "complex nmf under phase constraints based on signal modeling application to audio source separation"
    - "phase reconstruction of spectrograms based on a model of repeated audio events"
    - "informed source separation using iterative reconstruction"
    - "wirtinger s calculus in general hilbert spaces"

Connected through hyperedge HE-4 (induced by source paper "phase recovery by unwrapping applications to music signal processing"):
  with members:
    - "multi scale multi band densenets for audio source separation"
    - "phase reconstruction of spectrograms with linear unwrapping application to audio signal restoration"
    - "complex nmf under phase constraints based on signal modeling application to audio source separation"
    - "phase reconstruction of spectrograms based on a model of repeated audio events"
    - "wirtinger s calculus in general hilbert spaces"

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)