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: "the capio 2017 conversational speech recognition system"
  Abstract: In this paper we show how we have achieved the state-of-the-art performance on the industry-standard NIST 2000 Hub5 English evaluation set. We explore densely connected LSTMs, inspired by the densely connected convolutional networks recently introduced for image classification tasks. We also propose an acoustic model adaptation scheme that simply averages the parameters of a seed neural network acoustic model and its adapted version. This method was applied with the CallHome training corpus and improved individual system performances by on average 6.1% (relative) against the CallHome portion of the evaluation set with no performance loss on the Switchboard portion. With RNN-LM rescoring and lattice combination on the 5 systems trained across three different phone sets, our 2017 speech recognition system has obtained 5.0% and 9.1% on Switchboard and CallHome, respectively, both of which are the best word error rates reported thus far. According to IBM in their latest work to compare human and machine transcriptions, our reported Switchboard word error rate can be considered to surpass the human parity (5.1%) of transcribing conversational telephone speech.

Connected through hyperedge HE-1 (induced by source paper "sequence to sequence speech recognition with time depth separable convolutions"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "fully convolutional speech recognition"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "wav2letter the fastest open source speech recognition system"
    - "exploring the limits of weakly supervised pretraining"
    - "improved training of end to end attention models for speech recognition"
    - "understanding back translation at scale"

Connected through hyperedge HE-2 (induced by source paper "who needs words lexicon free speech recognition"):
  with members:
    - "fully convolutional speech recognition"
    - "wav2letter the fastest open source speech recognition system"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "comparison of decoding strategies for ctc acoustic models"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "towards end to end speech recognition with deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "end to end asr from supervised to semi supervised learning with modern architectures"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "reducing transformer depth on demand with structured dropout"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "word level speech recognition with a dynamic lexicon"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self training for end to end speech recognition"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"

Connected through hyperedge HE-4 (induced by source paper "on the choice of modeling unit for sequence to sequence speech recognition"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "improved training of end to end attention models for speech recognition"
    - "optimal completion distillation for sequence learning"
    - "sequence to sequence models can directly translate foreign speech"
    - "state of the art speech recognition with sequence to sequence models"
    - "minimum word error rate training for attention based sequence to sequence models"
    - "no need for a lexicon evaluating the value of the pronunciation lexica in end to end models"

Connected through hyperedge HE-5 (induced by source paper "rwth asr systems for librispeech hybrid vs attention w o data augmentation"):
  with members:
    - "on the choice of modeling unit for sequence to sequence speech recognition"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "language modeling with deep transformers"
    - "fully convolutional speech recognition"
    - "returnn as a generic flexible neural toolkit with application to translation and speech recognition"
    - "improved training of end to end attention models for speech recognition"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "optimal completion distillation for sequence learning"

Connected through hyperedge HE-6 (induced by source paper "improved knowledge distillation via teacher assistant"):
  with members:
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "large scale distributed neural network training through online distillation"
    - "kickstarting deep reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "nisp pruning networks using neuron importance score propagation"
    - "deep mutual learning"
    - "visual relationship detection with internal and external linguistic knowledge distillation"

Connected through hyperedge HE-7 (induced by source paper "transformers with convolutional context for asr"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "espnet end to end speech processing toolkit"
    - "fully convolutional speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentional acoustic models"
    - "improved training of end to end attention models for speech recognition"
    - "optimal completion distillation for sequence learning"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"):
  with members:
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "a comparative study on transformer vs rnn in speech applications"
    - "transformer xl attentive language models beyond a fixed length context"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a novel pyramidal fsmn architecture with lattice free mmi for speech recognition"
    - "attention is all you need"

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)