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 "filterbank design for end to end speech separation"):
  Abstract: Single-channel speech separation has recently made great progress thanks to learned filterbanks as used in ConvTasNet. In parallel, parameterized filterbanks have been proposed for speaker recognition where only center frequencies and bandwidths are learned. In this work, we extend real-valued learned and parameterized filterbanks into complex-valued analytic filterbanks and define a set of corresponding representations and masking strategies. We evaluate these filterbanks on a newly released noisy speech separation dataset (WHAM). The results show that the proposed analytic learned filterbank consistently outperforms the real-valued filterbank of ConvTasNet. Also, we validate the use of parameterized filterbanks and show that complex-valued representations and masks are beneficial in all conditions. Finally, we show that the STFT achieves its best performance for 2ms windows.

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 "wham extending speech separation to noisy environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "continuous speech separation dataset and analysis")
  Member "on the variance of the adaptive learning rate and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "lookahead optimizer k steps forward 1 step back":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "sdr half baked or well done":
    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 "deep attractor network for single microphone speaker separation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (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)