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: "beyond error propagation in neural machine translation characteristics of language also matter"
  Abstract: Neural machine translation usually adopts autoregressive models and suffers from exposure bias as well as the consequent error propagation problem. Many previous works have discussed the relationship between error propagation and the \emph{accuracy drop} (i.e., the left part of the translated sentence is often better than its right part in left-to-right decoding models) problem. In this paper, we conduct a series of analyses to deeply understand this problem and get several interesting findings. (1) The role of error propagation on accuracy drop is overstated in the literature, although it indeed contributes to the accuracy drop problem. (2) Characteristics of a language play a more important role in causing the accuracy drop: the left part of the translation result in a right-branching language (e.g., English) is more likely to be more accurate than its right part, while the right part is more accurate for a left-branching language (e.g., Japanese). Our discoveries are confirmed on different model structures including Transformer and RNN, and in other sequence generation tasks such as text summarization.

Connected through hyperedge HE-1 (induced by source paper "insertion based decoding with automatically inferred generation order"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "synchronous bidirectional neural machine translation"
    - "sequence generation from both sides to the middle"
    - "non monotonic sequential text generation"
    - "text infilling"
    - "self attention with relative position representations"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a tree based decoder for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "hard but robust easy but sensitive how encoder and decoder perform in neural machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "hybrid self attention network for machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "dense information flow for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "acquiring knowledge from pre trained model to neural machine translation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "learning representation mapping for relation detection in knowledge base question answering"
    - "mass masked sequence to sequence pre training for language generation"
    - "dynamic past and future for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "investigating backtranslation in neural machine translation"
    - "exploiting deep representations for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "improving neural machine translation with pre trained representation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "investigating backtranslation in neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "multilingual neural machine translation with language clustering"):
  with members:
    - "unsupervised pivot translation for distant languages"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "language graph distillation for low resource machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "dense information flow for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "sequence modeling with unconstrained generation order"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "non monotonic sequential text generation"
    - "universal transformers"
    - "middle out decoding"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-7 (induced by source paper "efficient bidirectional neural machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "achieving human parity on automatic chinese to english news translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "dense information flow for neural machine translation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "towards neural phrase based machine translation"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "non autoregressive neural machine translation with enhanced decoder input"):
  with members:
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "analyzing uncertainty in neural machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "fast decoding in sequence models using discrete latent variables"
    - "double path networks for sequence to sequence learning"
    - "dense information flow for neural machine translation"
    - "attention is all you need"
    - "non autoregressive 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)