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 "interrupted and cascaded permutation invariant training for speech separation"):
  Abstract: Permutation Invariant Training (PIT) has long been a stepping stone method for training speech separation model in handling the label ambiguity problem. With PIT selecting the minimum cost label assignments dynamically, very few studies considered the separation problem to be optimizing both the model parameters and the label assignments, but focused on searching for good model architecture and parameters. In this paper, we investigate instead for a given model architecture the various flexible label assignment strategies for training the model, rather than directly using PIT. Surprisingly, we discover a significant performance boost compared to PIT is possible if the model is trained with fixed label assignments and a good set of labels is chosen. With fixed label training cascaded between two sections of PIT, we achieved the state-of-the-art performance on WSJ0-2mix without changing the model architecture at all.

Includes members:
  Member "conv tasnet surpassing ideal time frequency magnitude masking for speech separation":
    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 "improved speech separation with time and frequency cross domain joint embedding and clustering":
    also connected through other hyperedges:
      (none)
  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 speech separation with unfolded iterative phase reconstruction":
    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 learning based phase reconstruction for speaker separation a trigonometric perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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 "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")

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)