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 "end to end speech separation with unfolded iterative phase reconstruction"):
  Abstract: This paper proposes an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transform (STFT), and its inverse are represented as layers within a deep network. Previous approaches, rather than computing a loss on the reconstructed signal, used a surrogate loss based on the target STFT magnitudes. This ignores reconstruction error introduced by phase inconsistency. In our approach, the loss function is directly defined on the reconstructed signals, which are optimized for best separation. In addition, we train through unfolded iterations of a phase reconstruction algorithm, represented as a series of STFT and inverse STFT layers. While mask values are typically limited to lie between zero and one for approaches using the mixture phase for reconstruction, this limitation is less relevant if the estimated magnitudes are to be used together with phase reconstruction. We thus propose several novel activation functions for the output layer of the T-F masking, to allow mask values beyond one. On the publicly-available wsj0-2mix dataset, our approach achieves state-of-the-art 12.6 dB scale-invariant signal-to-distortion ratio (SI-SDR) and 13.1 dB SDR, revealing new possibilities for deep learning based phase reconstruction and representing a fundamental progress towards solving the notoriously-hard cocktail party problem.

Includes members:
  Member "sdr half baked or well done":
    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 "speaker independent speech separation with deep attractor network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supervised speaker embedding de mixing in two speaker environment")
  Member "end to end source separation with adaptive front ends":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "deep attractor network for single microphone speaker separation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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")
  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 "tasnet time domain audio separation network for real time single channel speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "permutation invariant training of deep models for speaker independent multi talker 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")
  Member "single channel multi speaker separation using deep clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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")

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)