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: "recognizing multi talker speech with permutation invariant training"
  Abstract: In this paper, we propose a novel technique for direct recognition of multiple speech streams given the single channel of mixed speech, without first separating them. Our technique is based on permutation invariant training (PIT) for automatic speech recognition (ASR). In PIT-ASR, we compute the average cross entropy (CE) over all frames in the whole utterance for each possible output-target assignment, pick the one with the minimum CE, and optimize for that assignment. PIT-ASR forces all the frames of the same speaker to be aligned with the same output layer. This strategy elegantly solves the label permutation problem and speaker tracing problem in one shot. Our experiments on artificially mixed AMI data showed that the proposed approach is very promising.

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"
    - "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"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "utterance level permutation invariant training with latency controlled blstm for single channel multi talker speech separation"):
  with members:
    - "end to end monaural multi speaker asr system without pretraining"
    - "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"
    - "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-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"
    - "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"
    - "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"
    - "a purely end to end system for multi speaker speech recognition"
    - "deep attractor network for single microphone speaker separation"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-6 (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"
    - "a purely end to end system for multi speaker 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"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"

Connected through hyperedge HE-7 (induced by source paper "progressive joint modeling in unsupervised single channel overlapped speech recognition"):
  with members:
    - "deep attractor network for single microphone speaker separation"
    - "single channel multi talker speech recognition with permutation invariant training"
    - "large scale domain adaptation via teacher student learning"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "small footprint highway deep neural networks for speech recognition"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "single channel multi speaker separation using deep clustering"
    - "achieving human parity in conversational speech recognition"

Connected through hyperedge HE-8 (induced by source paper "single channel multi talker speech recognition with permutation invariant training"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "deep attractor network for single microphone speaker separation"
    - "dense prediction on sequences with time dilated convolutions for speech recognition"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "single channel multi speaker separation using deep clustering"
    - "very deep convolutional neural networks for robust speech recognition"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "the microsoft 2016 conversational speech recognition system"

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)