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 "on single channel speech enhancement and on non linear modulation domain kalman filtering"):
  Abstract: This report focuses on algorithms that perform single-channel speech enhancement. The author of this report uses modulation-domain Kalman filtering algorithms for speech enhancement, i.e. noise suppression and dereverberation, in [1], [2], [3], [4] and [5]. Modulation-domain Kalman filtering can be applied for both noise and late reverberation suppression and in [2], [1], [3] and [4], various model-based speech enhancement algorithms that perform modulation-domain Kalman filtering are designed, implemented and tested. The model-based enhancement algorithm in [2] estimates and tracks the speech phase. The short-time-Fourier-transform-based enhancement algorithm in [5] uses the active speech level estimator presented in [6]. This report describes how different algorithms perform speech enhancement and the algorithms discussed in this report are addressed to researchers interested in monaural speech enhancement. The algorithms are composed of different processing blocks and techniques [7]; understanding the implementation choices made during the system design is important because this provides insights that can assist the development of new algorithms. Index Terms - Speech enhancement, dereverberation, denoising, Kalman filter, minimum mean squared error estimation.

Includes members:
  Member "phasebook and friends leveraging discrete representations for source separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "modulation domain kalman filtering for monaural blind speech denoising and dereverberation":
    also connected through other hyperedges:
      (none)
  Member "on the importance of super gaussian speech priors for machine learning based speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modulation domain kalman filtering for monaural blind speech denoising and dereverberation")
  Member "model based speech enhancement in the modulation domain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modulation domain kalman filtering for monaural blind speech denoising and dereverberation")
  Member "deep learning for environmentally robust speech recognition an overview of recent developments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "single channel speech separation with auxiliary speaker embeddings")
  Member "speech dereverberation using non negative convolutive transfer function and spectro temporal modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "investigating generative adversarial networks based speech dereverberation for robust speech recognition")
  Member "speech enhancement in multiple noise conditions using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving gans for speech enhancement")
  Member "a neural network alternative to non negative audio models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep audio prior")

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)