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 "progressive joint modeling in unsupervised single channel overlapped speech recognition"):
  Abstract: Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of the art model-based approach, which applies a single neural network to solve this single-input, multiple-output modeling problem. We propose to advance the current state of the art by imposing a modular structure on the neural network, applying a progressive pretraining regimen, and improving the objective function with transfer learning and a discriminative training criterion. The modular structure splits the problem into three subtasks: frame-wise interpreting, utterance-level speaker tracing, and speech recognition. The pretraining regimen uses these modules to solve progressively harder tasks. Transfer learning leverages parallel clean speech to improve the training targets for the network. Our discriminative training formulation is a modification of standard formulations that also penalizes competing outputs of the system. Experiments are conducted on the artificial overlapped switchboard and hub5e-swb dataset. The proposed framework achieves over 30% relative improvement of word error rate over both a strong jointly trained system, PIT for ASR, and a separately optimized system, PIT for speech separation with clean speech ASR model. The improvement comes from better model generalization, training efficiency, and the sequence level linguistic knowledge integration.

Includes members:
  Member "deep attractor network for single microphone speaker separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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 "single channel multi talker speech recognition with permutation invariant training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "conv tasnet surpassing ideal time frequency magnitude masking for speech separation")
  Member "large scale domain adaptation via teacher student learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fully learnable front end for multi channel acoustic modeling using semi supervised learning")
  Member "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "recognizing multi talker speech with permutation invariant training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "auxiliary interference speaker loss for target speaker speech recognition")
  Member "small footprint highway deep neural networks for speech recognition":
    also connected through other hyperedges:
      (none)
  Member "permutation invariant training of deep models for speaker independent multi talker 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 "single channel multi speaker separation using deep clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.CL(Computation and Language)