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 "dilated convolution with dilated gru for music source separation"):
  Abstract: Stacked dilated convolutions used in Wavenet have been shown effective for generating high-quality audios. By replacing pooling/striding with dilation in convolution layers, they can preserve high-resolution information and still reach distant locations. Producing high-resolution predictions is also crucial in music source separation, whose goal is to separate different sound sources while maintaining the quality of the separated sounds. Therefore, this paper investigates using stacked dilated convolutions as the backbone for music source separation. However, while stacked dilated convolutions can reach wider context than standard convolutions, their effective receptive fields are still fixed and may not be wide enough for complex music audio signals. To reach information at remote locations, we propose to combine dilated convolution with a modified version of gated recurrent units (GRU) called the `Dilated GRU' to form a block. A Dilated GRU unit receives information from k steps before instead of the previous step for a fixed k. This modification allows a GRU unit to reach a location with fewer recurrent steps and run faster because it can execute partially in parallel. We show that the proposed model with a stack of such blocks performs equally well or better than the state-of-the-art models for separating vocals and accompaniments.

Includes members:
  Member "an overview of lead and accompaniment separation in music":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "addressing the confounds of accompaniments in singer identification")
  Member "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time domain audio source separation based on wave u net combined with discrete wavelet transform")
  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 "denoising auto encoder with recurrent skip connections and residual regression for music source separation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "addressing the confounds of accompaniments in singer identification")
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "feudal networks for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "multi scale multi band densenets for audio source separation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")

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)