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: "six challenges for neural machine translation"
  Abstract: We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.

Connected through hyperedge HE-1 (induced by source paper "a brief survey of multilingual neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "the missing ingredient in zero shot neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "zero resource multilingual model transfer learning what to share"

Connected through hyperedge HE-2 (induced by source paper "non autoregressive neural dialogue generation"):
  with members:
    - "avgout a simple output probability measure to eliminate dull responses"
    - "towards a human like open domain chatbot"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "comparison of diverse decoding methods from conditional language models"
    - "fast structured decoding for sequence models"
    - "complexity weighted loss and diverse reranking for sentence simplification"
    - "pnat non autoregressive transformer by position learning"
    - "dialogpt large scale generative pre training for conversational response generation"

Connected through hyperedge HE-3 (induced by source paper "lava nat a non autoregressive translation model with look around decoding and vocabulary attention"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "hint based training for non autoregressive machine translation"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "fast structured decoding for sequence models"
    - "pnat non autoregressive transformer by position learning"
    - "levenshtein transformer"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "self attention with relative position representations"

Connected through hyperedge HE-4 (induced by source paper "a multilingual view of unsupervised machine translation"):
  with members:
    - "a probabilistic formulation of unsupervised text style transfer"
    - "multilingual denoising pre training for neural machine translation"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "explicit cross lingual pre training for unsupervised machine translation"
    - "an effective approach to unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "consistency by agreement in zero shot neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation"):
  with members:
    - "morphological word embeddings"
    - "character aware decoder for translation into morphologically rich languages"
    - "massively parallel cross lingual learning in low resource target language translation"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "multi way multilingual neural machine translation with a shared attention mechanism"

Connected through hyperedge HE-6 (induced by source paper "learning to multi task learn for better neural machine translation"):
  with members:
    - "autosem automatic task selection and mixing in multi task learning"
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "learning to reweight examples for robust deep learning"
    - "scheduled multi task learning from syntax to translation"
    - "learning to multitask"
    - "opennmt open source toolkit for neural machine translation"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-7 (induced by source paper "membership inference attacks on sequence to sequence models is my data in your machine translation system"):
  with members:
    - "openkiwi an open source framework for quality estimation"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "algorithms that remember model inversion attacks and data protection law"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "dual conditional cross entropy filtering of noisy parallel corpora"
    - "understanding membership inferences on well generalized learning models"
    - "the unintended consequences of overfitting training data inference attacks"

Connected through hyperedge HE-8 (induced by source paper "urdu english machine transliteration using neural networks"):
  with members:
    - "how much does tokenization affect neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "beam search strategies for neural machine translation"
    - "natural language processing state of the art current trends and challenges"
    - "neural machine transliteration preliminary results"
    - "transfer learning for low resource neural machine translation"
    - "polish to english statistical 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)