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 "robust coherence based spectral enhancement for speech recognition in adverse real world environments"):
  Abstract: Abstract   Speech recognition in adverse real-world environments is highly affected by reverberation and non-stationary background noise. A well-known strategy to reduce such undesired signal components in multi-microphone scenarios is spatial filtering of the microphone signals. In this article, we demonstrate that an additional coherence-based postfilter, which is applied to the beamformer output signal to remove diffuse interference components from the latter, is an effective means to further improve the recognition accuracy of modern deep learning speech recognition systems. To this end, the 3rd CHiME Speech Separation and Recognition Challenge (CHiME-3) baseline speech enhancement system is extended by a coherence-based postfilter and the postfilter’s impact on the Word Error Rates (WERs) of a state-of-the-art automatic speech recognition system is investigated for the realistic noisy environments provided by CHiME-3. To determine the time- and frequency-dependent postfilter gains, we use Direction-of-Arrival (DOA)-dependent and (DOA)-independent estimators of the coherent-to-diffuse power ratio as an approximation of the short-time signal-to-noise ratio. Our experiments show that incorporating coherence-based postfiltering into the CHiME-3 baseline speech enhancement system leads to a significant reduction of the WERs, with relative improvements of up to 11.31%.

Includes members:
  Member "the fifth chime speech separation and recognition challenge dataset task and baselines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "spatial diffuseness features for dnn based speech recognition in noisy and reverberant environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust sound event detection in bioacoustic sensor networks")
  Member "coherent to diffuse power ratio estimation for dereverberation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "narrow band deep filtering for multichannel speech enhancement")

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)