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: "guided alignment training for topic aware neural machine translation"
  Abstract: In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models. We show that our novel guided alignment training approach improves translation quality on real-life e-commerce texts consisting of product titles and descriptions, overcoming the problems posed by many unknown words and a large type/token ratio. We also show that meta-data associated with input texts such as topic or category information can significantly improve translation quality when used as an additional signal to the decoder part of the network. With both novel features, the BLEU score of the NMT system on a product title set improves from 18.6 to 21.3%. Even larger MT quality gains are obtained through domain adaptation of a general domain NMT system to e-commerce data. The developed NMT system also performs well on the IWSLT speech translation task, where an ensemble of four variant systems outperforms the phrase-based baseline by 2.1% BLEU absolute.

Connected through hyperedge HE-1 (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-2 (induced by source paper "an empirical study of spatial attention mechanisms in deep networks"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "attention is not explanation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ocnet object context network for scene parsing"
    - "self attention with relative position representations"
    - "ccnet criss cross attention for semantic segmentation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "learning region features for object detection"

Connected through hyperedge HE-3 (induced by source paper "character based nmt with transformer"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "learning deep transformer models for machine translation"
    - "improving robustness of machine translation with synthetic noise"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "training deeper neural machine translation models with transparent attention"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-4 (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-5 (induced by source paper "gasl guided attention for sparsity learning in deep neural networks"):
  with members:
    - "attention is all you need"
    - "learning structured sparsity in deep neural networks"
    - "attention correctness in neural image captioning"
    - "neural machine translation with supervised attention"
    - "supervised attentions for neural machine translation"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "convolutional neural networks with low rank regularization"

Connected through hyperedge HE-6 (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-7 (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"

Connected through hyperedge HE-8 (induced by source paper "learning to deceive with attention based explanations"):
  with members:
    - "attention is not not explanation"
    - "on identifiability in transformers"
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "on the validity of self attention as explanation in transformer models"
    - "attention is not explanation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "an interpretable knowledge transfer model for knowledge base completion"

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)