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 "simultaneous speech recognition and speaker diarization for monaural dialogue recordings with target speaker acoustic models"):
  Abstract: This paper investigates the use of target-speaker automatic speech recognition (TS-ASR) for simultaneous speech recognition and speaker diarization of single-channel dialogue recordings. TS-ASR is a technique to automatically extract and recognize only the speech of a target speaker given a short sample utterance of that speaker. One obvious drawback of TS-ASR is that it cannot be used when the speakers in the recordings are unknown because it requires a sample of the target speakers in advance of decoding. To remove this limitation, we propose an iterative method, in which (i) the estimation of speaker embeddings and (ii) TS-ASR based on the estimated speaker embeddings are alternately executed. We evaluated the proposed method by using very challenging dialogue recordings in which the speaker overlap ratio was over 20%. We confirmed that the proposed method significantly reduced both the word error rate (WER) and diarization error rate (DER). Our proposed method combined with i-vector speaker embeddings ultimately achieved a WER that differed by only 2.1 % from that of TS-ASR given oracle speaker embeddings. Furthermore, our method can solve speaker diarization simultaneously as a by-product and achieved better DER than that of the conventional clustering-based speaker diarization method based on i-vector.

Includes members:
  Member "auxiliary interference speaker loss for target speaker speech recognition":
    also connected through other hyperedges:
      (none)
  Member "a comparison of lattice free discriminative training criteria for purely sequence trained neural network acoustic models":
    also connected through other hyperedges:
      (none)
  Member "end to end monaural multi speaker asr system without pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "auxiliary interference speaker loss for target speaker speech recognition")
  Member "progressive joint modeling in unsupervised single channel overlapped speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "auxiliary interference speaker loss for target speaker speech recognition")
  Member "a purely end to end system for multi speaker speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "auxiliary interference speaker loss for target speaker speech recognition")
  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 "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 "deep clustering discriminative embeddings for segmentation and 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")

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)