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: "single channel multi talker speech recognition with permutation invariant training"
  Abstract: Although great progresses have been made in automatic speech recognition (ASR), significant performance degradation is still observed when recognizing multi-talker mixed speech. In this paper, we propose and evaluate several architectures to address this problem under the assumption that only a single channel of mixed signal is available. Our technique extends permutation invariant training (PIT) by introducing the front-end feature separation module with the minimum mean square error (MSE) criterion and the back-end recognition module with the minimum cross entropy (CE) criterion. More specifically, during training we compute the average MSE or CE over the whole utterance for each possible utterance-level output-target assignment, pick the one with the minimum MSE or CE, and optimize for that assignment. This strategy elegantly solves the label permutation problem observed in the deep learning based multi-talker mixed speech separation and recognition systems. The proposed architectures are evaluated and compared on an artificially mixed AMI dataset with both two- and three-talker mixed speech. The experimental results indicate that our proposed architectures can cut the word error rate (WER) by 45.0% and 25.0% relatively against the state-of-the-art single-talker speech recognition system across all speakers when their energies are comparable, for two- and three-talker mixed speech, respectively. To our knowledge, this is the first work on the multi-talker mixed speech recognition on the challenging speaker-independent spontaneous large vocabulary continuous speech task.

Connected through hyperedge HE-1 (induced by source paper "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "speaker independent speech separation with deep attractor network"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "cracking the cocktail party problem by multi beam deep attractor network"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "end to end source separation with adaptive front ends"
    - "deep attractor network for single microphone speaker separation"

Connected through hyperedge HE-2 (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"
    - "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-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"
    - "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-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "progressive joint modeling in unsupervised single channel overlapped speech recognition"):
  with members:
    - "deep attractor network for single microphone speaker separation"
    - "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"
    - "recognizing multi talker speech with permutation invariant training"
    - "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-6 (induced by source paper "a purely end to end system for multi speaker speech recognition"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "deep attractor network for single microphone speaker separation"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "chainermn scalable distributed deep learning framework"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "end to end attention based large vocabulary speech recognition"
    - "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.SD(Sound)