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: "a purely end to end system for multi speaker speech recognition"
  Abstract: Recently, there has been growing interest in multi-speaker speech recognition, where the utterances of multiple speakers are recognized from their mixture. Promising techniques have been proposed for this task, but earlier works have required additional training data such as isolated source signals or senone alignments for effective learning. In this paper, we propose a new sequence-to-sequence framework to directly decode multiple label sequences from a single speech sequence by unifying source separation and speech recognition functions in an end-to-end manner. We further propose a new objective function to improve the contrast between the hidden vectors to avoid generating similar hypotheses. Experimental results show that the model is directly able to learn a mapping from a speech mixture to multiple label sequences, achieving 83.1 % relative improvement compared to a model trained without the proposed objective. Interestingly, the results are comparable to those produced by previous end-to-end works featuring explicit separation and recognition modules.

Connected through hyperedge HE-1 (induced by source paper "auxiliary interference speaker loss for target speaker speech recognition"):
  with members:
    - "end to end monaural multi speaker asr system without pretraining"
    - "progressive joint modeling in unsupervised single channel overlapped 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"
    - "end to end monaural multi speaker asr system without pretraining"
    - "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"
    - "end to end monaural multi speaker asr system without pretraining"
    - "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 "unsupervised training of a deep clustering model for multichannel blind source separation"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "deep attractor network for single microphone speaker separation"
    - "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"
    - "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"
    - "end to end monaural multi speaker asr system without pretraining"
    - "progressive joint modeling in unsupervised single channel overlapped 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"

Connected through hyperedge HE-6 (induced by source paper "sms wsj database performance measures and baseline recipe for multi channel source separation and recognition"):
  with members:
    - "wham extending speech separation to noisy environments"
    - "sdr half baked or well done"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-7 (induced by source paper "end to end monaural multi speaker asr system without pretraining"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "improved training of end to end attention models for speech recognition"
    - "single channel multi talker speech recognition with permutation invariant training"
    - "attention is all you need"
    - "recognizing multi talker speech with permutation invariant training"
    - "permutation invariant training of deep models for speaker independent multi talker speech 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.SD(Sound)