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 with supervised attention"
  Abstract: The attention mechanisim is appealing for neural machine translation, since it is able to dynam- ically encode a source sentence by generating a alignment between a target word and source words. Unfortunately, it has been proved to be worse than conventional alignment models in aligment accuracy. In this paper, we analyze and explain this issue from the point view of re- ordering, and propose a supervised attention which is learned with guidance from conventional alignment models. Experiments on two Chinese-to-English translation tasks show that the super- vised attention mechanism yields better alignments leading to substantial gains over the standard attention based NMT.

Connected through hyperedge HE-1 (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-2 (induced by source paper "relevance promoting language model for short text conversation"):
  with members:
    - "generating multiple diverse responses with multi mapping and posterior mapping selection"
    - "dlgnet a transformer based model for dialogue response generation"
    - "the curious case of neural text degeneration"
    - "comparison of diverse decoding methods from conditional language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "a discrete cvae for response generation on short text conversation"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "dialogpt large scale generative pre training for conversational response generation"

Connected through hyperedge HE-3 (induced by source paper "controlling utterance length in nmt based word segmentation with attention"):
  with members:
    - "attention is not not explanation"
    - "attention is not explanation"
    - "tied multitask learning for neural speech translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "low resource speech to text translation"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-4 (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"
    - "neural machine translation decoding with terminology constraints"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "shared private bilingual word embeddings for neural machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "universal neural machine translation for extremely low resource languages"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "thumt an open source toolkit for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "slim embedding layers for recurrent neural language models"

Connected through hyperedge HE-6 (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"
    - "guided alignment training for topic aware neural machine translation"
    - "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-7 (induced by source paper "in training matrix factorization for parameter frugal neural machine translation"):
  with members:
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "training tips for the transformer model"
    - "pieces of eight 8 bit neural machine translation"
    - "attention is all you need"
    - "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
    - "opennmt open source toolkit for neural machine translation"
    - "unfolding and shrinking neural machine translation ensembles"
    - "factorization tricks for lstm networks"

Connected through hyperedge HE-8 (induced by source paper "associating natural language comment and source code entities"):
  with members:
    - "structured neural summarization"
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "the adverse effects of code duplication in machine learning models of code"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "typesql knowledge based type aware neural text to sql generation"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "retrieval based neural code generation"

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)