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: "neural machine translation decoding with terminology constraints"
  Abstract: Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constraints remains an open problem. We describe our approach to constrained neural decoding based on finite-state machines and multi-stack decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. We demonstrate the performance of our framework on multiple translation tasks and motivate the need for constrained decoding with attentions as a means of reducing misplacement and duplication when translating user constraints.

Connected through hyperedge HE-1 (induced by source paper "commongen a constrained text generation dataset towards generative commonsense reasoning"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "codah an adversarially authored question answer dataset for common sense"
    - "bertscore evaluating text generation with bert"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "mirrorgan learning text to image generation by redescription"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "levenshtein transformer"

Connected through hyperedge HE-2 (induced by source paper "saliency driven word alignment interpretation for neural machine translation"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "methods for interpreting and understanding deep neural networks"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
    - "smoothgrad removing noise by adding noise"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"

Connected through hyperedge HE-3 (induced by source paper "learning to reuse translations guiding neural machine translation with examples"):
  with members:
    - "exploiting deep representations for neural machine translation"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "tied multitask learning for neural speech translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "improving the transformer translation model with document level context"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "fusing recency into neural machine translation with an inter sentence gate model"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "code switching for enhancing nmt with pre specified translation"):
  with members:
    - "on the alignment problem in multi head attention based neural machine translation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "get to the point summarization with pointer generator networks"
    - "searching for activation functions"
    - "pointing the unknown words"
    - "neural machine translation advised by statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "neural machine translation with noisy lexical constraints"):
  with members:
    - "non parametric adaptation for neural machine translation"
    - "training neural machine translation to apply terminology constraints"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "quickedit editing text translations by crossing words out"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "lexically constrained decoding for sequence generation using grid beam search"

Connected through hyperedge HE-6 (induced by source paper "pointer based fusion of bilingual lexicons into neural machine translation"):
  with members:
    - "code switching for enhancing nmt with pre specified translation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "get to the point summarization with pointer generator networks"
    - "pointing the unknown words"

Connected through hyperedge HE-7 (induced by source paper "correct and memorize learning to translate from interactive revisions"):
  with members:
    - "active learning for interactive neural machine translation of data streams"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation"
    - "learning to discriminate noises for incorporating external information in neural machine translation"
    - "online learning for neural machine translation post editing"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"

Connected through hyperedge HE-8 (induced by source paper "neural machine translation with explicit phrase alignment"):
  with members:
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "six challenges for neural machine translation"
    - "neural machine translation leveraging phrase based models in a hybrid search"
    - "syntactically guided neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation of rare words with subword units"

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)