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: "optimal completion distillation for sequence learning"
  Abstract: We present Optimal Completion Distillation (OCD), a training procedure for optimizing sequence to sequence models based on edit distance. OCD is efficient, has no hyper-parameters of its own, and does not require pretraining or joint optimization with conditional log-likelihood. Given a partial sequence generated by the model, we first identify the set of optimal suffixes that minimize the total edit distance, using an efficient dynamic programming algorithm. Then, for each position of the generated sequence, we use a target distribution that puts equal probability on the first token of all the optimal suffixes. OCD achieves the state-of-the-art performance on end-to-end speech recognition, on both Wall Street Journal and Librispeech datasets, achieving $9.3\%$ WER and $4.5\%$ WER respectively.

Connected through hyperedge HE-1 (induced by source paper "investigating the decoders of maximum likelihood sequence models a look ahead approach"):
  with members:
    - "on nmt search errors and model errors cat got your tongue"
    - "end to end dense video captioning with masked transformer"
    - "a call for clarity in reporting bleu scores"
    - "tvqa localized compositional video question answering"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "multi30k multilingual english german image descriptions"

Connected through hyperedge HE-2 (induced by source paper "insertion deletion transformer"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "non monotonic sequential text generation"
    - "levenshtein transformer"
    - "kermit generative insertion based modeling for sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "cost sensitive training for autoregressive models"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "on the weaknesses of reinforcement learning for neural machine translation"
    - "bridging the gap between training and inference for neural machine translation"
    - "non monotonic sequential text generation"
    - "beyond bleu training neural machine translation with semantic similarity"
    - "on nmt search errors and model errors cat got your tongue"
    - "loss functions for multiset prediction"
    - "attention is all you need"

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"
    - "the capio 2017 conversational speech recognition system"
    - "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"
    - "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"
    - "the capio 2017 conversational speech recognition system"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "levenshtein transformer"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "text repair model for neural machine translation"
    - "non monotonic sequential text generation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "bert pre training of deep bidirectional transformers for language understanding"

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"
    - "the capio 2017 conversational speech recognition system"
    - "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"
    - "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.LG(Machine Learning)