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: "progressive joint modeling in unsupervised single channel overlapped speech recognition"
  Abstract: Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of the art model-based approach, which applies a single neural network to solve this single-input, multiple-output modeling problem. We propose to advance the current state of the art by imposing a modular structure on the neural network, applying a progressive pretraining regimen, and improving the objective function with transfer learning and a discriminative training criterion. The modular structure splits the problem into three subtasks: frame-wise interpreting, utterance-level speaker tracing, and speech recognition. The pretraining regimen uses these modules to solve progressively harder tasks. Transfer learning leverages parallel clean speech to improve the training targets for the network. Our discriminative training formulation is a modification of standard formulations that also penalizes competing outputs of the system. Experiments are conducted on the artificial overlapped switchboard and hub5e-swb dataset. The proposed framework achieves over 30% relative improvement of word error rate over both a strong jointly trained system, PIT for ASR, and a separately optimized system, PIT for speech separation with clean speech ASR model. The improvement comes from better model generalization, training efficiency, and the sequence level linguistic knowledge integration.

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"
    - "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 "end to end anchored speech recognition"):
  with members:
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "end to end contextual speech recognition using class language models and a token passing decoder"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "deep extractor network for target speaker recovery from single channel speech mixtures"
    - "state of the art speech recognition with sequence to sequence models"
    - "end to end text dependent speaker verification"
    - "attention based models for speech recognition"
    - "adam a method for stochastic optimization"

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"
    - "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-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"
    - "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"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "lcanet end to end lipreading with cascaded attention ctc"):
  with members:
    - "draps dynamic and resource aware placement scheme for docker containers in a heterogeneous cluster"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "attention is all you need"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "end to end visual speech recognition with lstms"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "reconstruction for feature disentanglement in pose invariant face recognition"
    - "convolutional sequence to sequence learning"

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"
    - "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"
    - "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-8 (induced by source paper "a purely end to end system for multi speaker speech recognition"):
  with members:
    - "deep attractor network for single microphone speaker separation"
    - "single channel multi talker speech recognition with permutation invariant training"
    - "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.CL(Computation and Language)