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 "channel attention dense u net for multichannel speech enhancement"):
  Abstract: Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary mask that is applied to the mixture in the time-frequency domain to produce the clean speech. Despite the great performance in the single-channel setting, these frameworks lag in performance in the multichannel setting as the majority of these methods a) fail to exploit the available spatial information fully, and b) still treat the deep architecture as a black box which may not be well-suited for multichannel audio processing. This paper addresses these drawbacks, a) by utilizing complex ratio masking instead of masking on the magnitude of the spectrogram, and more importantly, b) by introducing a channel-attention mechanism inside the deep architecture to mimic beamforming. We propose Channel-Attention Dense U-Net, in which we apply the channel-attention unit recursively on feature maps at every layer of the network, enabling the network to perform non-linear beamforming. We demonstrate the superior performance of the network against the state-of-the-art approaches on the CHiME-3 dataset.

Includes members:
  Member "end to end multi channel speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a unified framework for speech separation")
  Member "unsupervised speech enhancement based on multichannel nmf informed beamforming for noise robust automatic speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "alternating between spectral and spatial estimation for speech separation and enhancement")
  Member "wave u net a multi scale neural network for end to end audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "source separation with deep generative priors")
  Member "the fifth chime speech separation and recognition challenge dataset task and baselines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "supervised speech separation based on deep learning an overview":
    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 "multi scale multi band densenets for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "a decomposable attention model for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lossless compression of deep neural networks")

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)