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: "model based speech enhancement in the modulation domain"
  Abstract: This paper presents an algorithm for modulation-domain speech enhancement using a Kalman filter. The proposed estimator jointly models the estimated dynamics of the spectral amplitudes of speech and noise to obtain an MMSE estimation of the speech amplitude spectrum with the assumption that the speech and noise are additive in the complex domain. In order to include the dynamics of noise amplitudes with those of speech amplitudes, we propose a statistical “Gaussring” model that comprises a mixture of Gaussians whose centers lie in a circle on the complex plane. The performance of the proposed algorithm is evaluated using the perceptual evaluation of speech quality measure, segmental SNR measure, and short-time objective intelligibility measure. For speech quality measures, the proposed algorithm is shown to give a consistent improvement over a wide range of SNRs when compared to competitive algorithms. Speech recognition experiments also show that the Gaussring-model-based algorithm performs well for two types of noise.

Connected through hyperedge HE-1 (induced by source paper "on single channel speech enhancement and on non linear modulation domain kalman filtering"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "modulation domain kalman filtering for monaural blind speech denoising and dereverberation"
    - "on the importance of super gaussian speech priors for machine learning based speech enhancement"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "speech dereverberation using non negative convolutive transfer function and spectro temporal modeling"
    - "speech enhancement in multiple noise conditions using deep neural networks"
    - "a neural network alternative to non negative audio models"

Connected through hyperedge HE-2 (induced by source paper "modulation domain kalman filtering for monaural blind speech denoising and dereverberation"):
  with members:
    - "on the importance of super gaussian speech priors for machine learning based speech enhancement"
    - "on single channel speech enhancement and on non linear modulation domain kalman filtering"
    - "phase aware single channel speech enhancement with modulation domain kalman filtering"
    - "speech enhancement in multiple noise conditions using deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "phase aware single channel speech enhancement with modulation domain kalman filtering"):
  with members:
    - "recursive bayesian filtering in circular state 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)