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: "incorporating discrete translation lexicons into neural machine translation"
  Abstract: Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understanding the meaning of the sentence. We propose a method to alleviate this problem by augmenting NMT systems with discrete translation lexicons that efficiently encode translations of these low-frequency words. We describe a method to calculate the lexicon probability of the next word in the translation candidate by using the attention vector of the NMT model to select which source word lexical probabilities the model should focus on. We test two methods to combine this probability with the standard NMT probability: (1) using it as a bias, and (2) linear interpolation. Experiments on two corpora show an improvement of 2.0-2.3 BLEU and 0.13-0.44 NIST score, and faster convergence time.

Connected through hyperedge HE-1 (induced by source paper "latent relation language models"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "bootstrapping generators from noisy data"
    - "neural lattice language models"
    - "describing a knowledge base"
    - "deep contextualized word representations"
    - "building language models for text with named entities"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"

Connected through hyperedge HE-2 (induced by source paper "rewe regressing word embeddings for regularization of neural machine translation systems"):
  with members:
    - "regularizing neural machine translation by target bidirectional agreement"
    - "training neural machine translation using word embedding based loss"
    - "token level and sequence level loss smoothing for rnn language models"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bag of words as target for neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "data augmentation for low resource neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "domain robustness in neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "a call for clarity in reporting bleu scores"
    - "bi directional differentiable input reconstruction for low resource neural machine translation"
    - "attention is all you need"
    - "extending defensive distillation"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "automatic post editing for machine translation"):
  with members:
    - "active learning for interactive neural machine translation of data streams"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "convolutional sequence to sequence learning"
    - "ensembling factored neural machine translation models for automatic post editing and quality estimation"
    - "pre translation for neural machine translation"
    - "systran s pure neural machine translation systems"

Connected through hyperedge HE-6 (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"
    - "neural machine translation decoding with terminology constraints"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a discriminative neural model for cross lingual word alignment"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross lingual argumentation mining machine translation and a bit of projection is all you need"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "jointly learning to align and translate with transformer models"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "linguistically informed self attention for semantic role labeling"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
    - "understanding back translation at scale"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"

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)