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 "la furca iterative context aware end to end monaural speech separation based on dual path deep parallel inter intra bi lstm with attention"):
  Abstract: Deep neural network with dual-path bi-directional long short-term memory (BiLSTM) block has been proved to be very effective in sequence modeling, especially in speech separation, e.g. DPRNN-TasNet \cite{luo2019dual}. In this paper, we propose several improvements of dual-path BiLSTM based network for end-to-end approach to monaural speech separation, which consists of 1) dual-path network with intra-parallel BiLSTM and inter-parallel BiLSTM components, 2) global context aware inter-intra cross-parallel BiLSTM, 3) local context-aware network with attention BiLSTM, 4) multiple spiral iterative refinement dual-path BiLSTM, that all these networks take the mixed utterance of two speakers and map it to two separated utterances, where each utterance contains only one speaker's voice. For the objective, we propose to train the network by directly optimizing utterance level signal-to-distortion ratio (SDR) in a permutation invariant training (PIT) style. Our experiments on the public WSJ0-2mix data corpus results in 19.86dB SDR improvement, which shows our proposed networks can lead to performance improvement on the speaker separation task. We have open-sourced our re-implementation of the DPRNN-TasNet in this https URL, and our `La Furca' is realized based on this implementation of DPRNN-TasNet, it is believed that the results in this paper can be smoothly reproduced.

Includes members:
  Member "is cqt more suitable for monaural speech separation than stft an empirical study":
    also connected through other hyperedges:
      (none)
  Member "universal sound separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a style transfer approach to source separation")
  Member "divide and conquer a deep casa approach to talker independent monaural speaker separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "two step sound source separation training on learned latent targets")
  Member "spatial temporal recurrent neural network for emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")
  Member "deep learning based phase reconstruction for speaker separation a trigonometric perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "deep attractor network for single microphone speaker separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "supervised speaker embedding de mixing in two speaker environment")
  Member "tasnet time domain audio separation network for real time single channel speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech 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)