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: "sequence to sequence mixture model for diverse machine translation"
  Abstract: Sequence to sequence (SEQ2SEQ) models often lack diversity in their generated translations. This can be attributed to the limitation of SEQ2SEQ models in capturing lexical and syntactic variations in a parallel corpus resulting from different styles, genres, topics, or ambiguity of the translation process. In this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that improves both translation diversity and quality by adopting a committee of specialized translation models rather than a single translation model. Each mixture component selects its own training dataset via optimization of the marginal loglikelihood, which leads to a soft clustering of the parallel corpus. Experiments on four language pairs demonstrate the superiority of our mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted beam search. Our mixture model uses negligible additional parameters and incurs no extra computation cost during decoding.

Connected through hyperedge HE-1 (induced by source paper "unsupervised data augmentation for consistency training"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "mixture models for diverse machine translation tricks of the trade"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-2 (induced by source paper "large scale pretraining for neural machine translation with tens of billions of sentence pairs"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "mass masked sequence to sequence pre training for language generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "is word segmentation necessary for deep learning of chinese representations"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "hard but robust easy but sensitive how encoder and decoder perform in neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"

Connected through hyperedge HE-3 (induced by source paper "mixture content selection for diverse sequence generation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "mixture models for diverse machine translation tricks of the trade"
    - "deep communicating agents for abstractive summarization"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "bottom up abstractive summarization"
    - "variational recurrent neural machine translation"
    - "a hierarchical latent structure for variational conversation modeling"
    - "hierarchical neural story generation"

Connected through hyperedge HE-4 (induced by source paper "generating diverse translation by manipulating multi head attention"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "mixture models for diverse machine translation tricks of the trade"
    - "investigating backtranslation in neural machine translation"
    - "d page diverse paraphrase generation"
    - "multi head attention with disagreement regularization"
    - "convolutional self attention network"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-5 (induced by source paper "mixture models for diverse machine translation tricks of the trade"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "achieving human parity on automatic chinese to english news translation"
    - "scaling neural machine translation"
    - "fast decoding in sequence models using discrete latent variables"
    - "hierarchical neural story generation"
    - "understanding back translation at scale"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "latent intention dialogue models"

Connected through hyperedge HE-6 (induced by source paper "flowseq non autoregressive conditional sequence generation with generative flow"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "macow masked convolutional generative flow"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "mask predict parallel decoding of conditional masked language models"
    - "mixture models for diverse machine translation tricks of the trade"

Connected through hyperedge HE-7 (induced by source paper "modeling fluency and faithfulness for diverse neural machine translation"):
  with members:
    - "bridging the gap between training and inference for neural machine translation"
    - "synchronous bidirectional inference for neural sequence generation"
    - "beyond bleu training neural machine translation with semantic similarity"
    - "achieving human parity on automatic chinese to english news translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "a study of reinforcement learning for neural machine translation"
    - "greedy search with probabilistic n gram matching for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "learning to make generalizable and diverse predictions for retrosynthesis"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "pre training graph neural networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "junction tree variational autoencoder for molecular graph generation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

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)