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: "improved training for online end to end speech recognition systems"
  Abstract: Achieving high accuracy with end-to-end speech recognizers requires careful parameter initialization prior to training. Otherwise, the networks may fail to find a good local optimum. This is particularly true for low-latency online networks, such as unidirectional LSTMs. Currently, the best strategy to train such systems is to bootstrap the training from a tied-triphone system. However, this is time consuming, and more importantly, is impossible for languages without a high-quality pronunciation lexicon. In this work, we propose an initialization strategy that uses teacher-student learning to transfer knowledge from a large, well-trained, offline end-to-end speech recognition model to an online end-to-end model, eliminating the need for a lexicon or any other linguistic resources. We also explore curriculum learning and label smoothing and show how they can be combined with the proposed teacher-student learning for further improvements. We evaluate our methods on a Microsoft Cortana personal assistant task and show that the proposed method results in a 19% relative improvement in word error rate compared to a randomly-initialized baseline system.

Connected through hyperedge HE-1 (induced by source paper "guiding ctc posterior spike timings for improved posterior fusion and knowledge distillation"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "residual convolutional ctc networks for automatic speech recognition"
    - "simplified end to end mmi training and voting for asr"
    - "advances in all neural speech recognition"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"

Connected through hyperedge HE-2 (induced by source paper "end to end speech recognition using a high rank lstm ctc based model"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "advancing connectionist temporal classification with attention modeling"
    - "improved training of end to end attention models for speech recognition"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "state of the art speech recognition with sequence to sequence models"
    - "improving end to end speech recognition with policy learning"
    - "exploring neural transducers for end to end speech recognition"
    - "improving the performance of online neural transducer models"

Connected through hyperedge HE-3 (induced by source paper "asr is all you need cross modal distillation for lip reading"):
  with members:
    - "deep audio visual speech recognition"
    - "guiding ctc posterior spike timings for improved posterior fusion and knowledge distillation"
    - "deep lip reading a comparison of models and an online application"
    - "large scale visual speech recognition"
    - "lrs3 ted a large scale dataset for visual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "voxceleb2 deep speaker recognition"

Connected through hyperedge HE-4 (induced by source paper "deep lip reading a comparison of models and an online application"):
  with members:
    - "end to end audiovisual speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "online and linear time attention by enforcing monotonic alignments"
    - "wav2letter an end to end convnet based speech recognition system"
    - "lipnet end to end sentence level lipreading"
    - "convolutional sequence to sequence learning"
    - "depthwise separable convolutions for neural machine translation"
    - "residual convolutional ctc networks for automatic speech recognition"

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)