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 "alternating between spectral and spatial estimation for speech separation and enhancement"):
  Abstract: This work investigates alternation between spectral separation using masking-based networks and spatial separation using multichannel beamforming. In this framework, the spectral separation is performed using a mask-based deep network. The result of mask-based separation is used, in turn, to estimate a spatial beamformer. The output of the beamformer is fed back into another mask-based separation network. We explore multiple ways of computing time-varying covariance matrices to improve beamforming, including factorizing the spatial covariance into a time-varying amplitude component and time-invariant spatial component. For the subsequent mask-based filtering, we consider different modes, including masking the noisy input, masking the beamformer output, and a hybrid approach combining both. Our best method first uses spectral separation, then spatial beamforming, and finally a spectral post-filter, and demonstrates an average improvement of 2.8 dB over baseline mask-based separation, across four different reverberant speech enhancement and separation tasks.

Includes members:
  Member "conv tasnet surpassing ideal time frequency magnitude masking for speech 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 "universal sound separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "unsupervised speech enhancement based on multichannel nmf informed beamforming for noise robust automatic speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "channel attention dense u net for multichannel speech enhancement")
  Member "libritts a corpus derived from librispeech for text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "comparison of speech representations for automatic quality estimation in multi speaker text to speech synthesis")
  Member "sdr half baked or well done":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "looking to listen at the cocktail party a speaker independent audio visual model for speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "deep learning based phase reconstruction for speaker separation a trigonometric perspective":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "differentiable consistency constraints for improved deep speech enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a style transfer approach to source separation")

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)