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: "developing far field speaker system via teacher student learning"
  Abstract: In this study, we develop the keyword spotting (KWS) and acoustic model (AM) components in a far-field speaker system. Specifically, we use teacher-student (T/S) learning to adapt a close-talk well-trained production AM to far-field by using parallel close-talk and simulated far-field data. We also use T/S learning to compress a large-size KWS model into a small-size one to fit the device computational cost. Without the need of transcription, T/S learning well utilizes untranscribed data to boost the model performance in both the AM adaptation and KWS model compression. We further optimize the models with sequence discriminative training and live data to reach the best performance of systems. The adapted AM improved from the baseline by 72.60% and 57.16% relative word error rate reduction on play-back and live test data, respectively. The final KWS model size was reduced by 27 times from a large-size KWS model without losing accuracy.

Connected through hyperedge HE-1 (induced by source paper "speaker adaptation for attention based end to end speech recognition"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "adversarial speaker adaptation"
    - "adversarial speaker verification"
    - "advancing acoustic to word ctc model"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "segan speech enhancement generative adversarial network"
    - "invariant representations for noisy speech recognition"

Connected through hyperedge HE-2 (induced by source paper "conditional teacher student learning"):
  with members:
    - "attentive adversarial learning for domain invariant training"
    - "adversarial speaker adaptation"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "a teacher student framework for zero resource neural machine translation"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "large scale domain adaptation via teacher student learning"
    - "sequence level knowledge distillation"
    - "knowledge distillation for small footprint highway networks"

Connected through hyperedge HE-3 (induced by source paper "advancing acoustic to word ctc model with attention and mixed units"):
  with members:
    - "advancing acoustic to word ctc model"
    - "advancing connectionist temporal classification with attention modeling"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-4 (induced by source paper "sequence discriminative training for deep learning based acoustic keyword spotting"):
  with members:
    - "a gpu based wfst decoder with exact lattice generation"
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "end to end asr free keyword search from speech"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "speech recognition with deep recurrent neural networks"
    - "sequence transduction with recurrent neural networks"

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)