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 reconstruction of spectrograms with linear unwrapping application to audio signal restoration"
  Abstract: This paper introduces a novel technique for reconstructing the phase of modified spectrograms of audio signals. From the analysis of mixtures of sinusoids we obtain relationships between phases of successive time frames in the Time-Frequency (TF) domain. To obtain similar relationships over frequencies, in particular within onset frames, we study an impulse model. Instantaneous frequencies and attack times are estimated locally to encompass the class of non-stationary signals such as vibratos. These techniques ensure both the vertical coherence of partials (over frequencies) and the horizontal coherence (over time). The method is tested on a variety of data and demonstrates better performance than traditional consistency-based approaches. We also introduce an audio restoration framework and observe that our technique outperforms traditional methods.

Connected through hyperedge HE-1 (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"
    - "complex nmf under phase constraints based on signal modeling application to audio source separation"
    - "model based stft phase recovery for audio source separation"
    - "phase recovery in nmf for audio source separation an insightful benchmark"

Connected through hyperedge HE-2 (induced by source paper "a noniterative method for reconstruction of phase from stft magnitude"):
  with members:
    - "sparse phase retrieval from short time fourier measurements"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

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"
    - "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"
    - "phase recovery in nmf for audio source separation an insightful benchmark"
    - "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"
    - "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"
    - "phase recovery in nmf for audio source separation an insightful benchmark"
    - "wirtinger s calculus in general hilbert spaces"

Connected through hyperedge HE-5 (induced by source paper "phase unmixing multichannel source separation with magnitude constraints"):
  with members:
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-6 (induced by source paper "complex nmf under phase constraints based on signal modeling application to audio source separation"):
  with members:
    - "phase reconstruction of spectrograms based on a model of repeated audio events"
    - "non negative matrix factorization with sparseness constraints"

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)