human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "end to end monaural multi speaker asr system without pretraining"
  Abstract: Recently, end-to-end models have become a popular approach as an alternative to traditional hybrid models in automatic speech recognition (ASR). The multi-speaker speech separation and recognition task is a central task in cocktail party problem. In this paper, we present a state-of-the-art monaural multi-speaker end-to-end automatic speech recognition model. In contrast to previous studies on the monaural multi-speaker speech recognition, this end-to-end framework is trained to recognize multiple label sequences completely from scratch. The system only requires the speech mixture and corresponding label sequences, without needing any indeterminate supervisions obtained from non-mixture speech or corresponding labels/alignments. Moreover, we exploited using the individual attention module for each separated speaker and the scheduled sampling to further improve the performance. Finally, we evaluate the proposed model on the 2-speaker mixed speech generated from the WSJ corpus and the wsj0-2mix dataset, which is a speech separation and recognition benchmark. The experiments demonstrate that the proposed methods can improve the performance of the end-to-end model in separating the overlapping speech and recognizing the separated streams. From the results, the proposed model leads to ~10.0% relative performance gains in terms of CER and WER respectively.

Connected through hyperedge HE-1 (induced by source paper "auxiliary interference speaker loss for target speaker speech recognition"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "a purely end to end system for multi speaker speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "deep attractor network for single microphone speaker separation"
    - "single channel multi talker speech recognition with permutation invariant training"
    - "english conversational telephone speech recognition by humans and machines"
    - "recognizing multi talker speech with permutation invariant training"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"

Connected through hyperedge HE-2 (induced by source paper "whamr noisy and reverberant single channel speech separation"):
  with members:
    - "furcanext end to end monaural speech separation with dynamic gated dilated temporal convolutional networks"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "wham extending speech separation to noisy environments"
    - "sdr half baked or well done"
    - "a purely end to end system for multi speaker speech recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "single channel multi speaker separation using deep clustering"

Connected through hyperedge HE-3 (induced by source paper "analysis of deep clustering as preprocessing for automatic speech recognition of sparsely overlapping speech"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "a purely end to end system for multi speaker speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "deep attractor network for single microphone speaker separation"
    - "the microsoft 2017 conversational speech recognition system"
    - "recognizing multi talker speech with permutation invariant training"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "single channel multi speaker separation using deep clustering"

Connected through hyperedge HE-4 (induced by source paper "utterance level permutation invariant training with latency controlled blstm for single channel multi talker speech separation"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "single channel multi talker speech recognition with permutation invariant training"
    - "the microsoft 2017 conversational speech recognition system"
    - "recognizing multi talker speech with permutation invariant training"
    - "tasnet time domain audio separation network for real time single channel speech separation"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "achieving human parity in conversational speech recognition"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-5 (induced by source paper "simultaneous speech recognition and speaker diarization for monaural dialogue recordings with target speaker acoustic models"):
  with members:
    - "auxiliary interference speaker loss for target speaker speech recognition"
    - "a comparison of lattice free discriminative training criteria for purely sequence trained neural network acoustic models"
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "a purely end to end system for multi speaker speech recognition"
    - "deep attractor network for single microphone speaker separation"
    - "recognizing multi talker speech with permutation invariant training"
    - "deep clustering discriminative embeddings for segmentation and separation"

Question: Please tell me which class the center node 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)