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: "vocabulary manipulation for neural machine translation"
  Abstract: In order to capture rich language phenomena, neural machine translation models have to use a large vocabulary size, which requires high computing time and large memory usage. In this paper, we alleviate this issue by introducing a sentence-level or batch-level vocabulary, which is only a very small sub-set of the full output vocabulary. For each sentence or batch, we only predict the target words in its sentencelevel or batch-level vocabulary. Thus, we reduce both the computing time and the memory usage. Our method simply takes into account the translation options of each word or phrase in the source sentence, and picks a very small target vocabulary for each sentence based on a wordto-word translation model or a bilingual phrase library learned from a traditional machine translation model. Experimental results on the large-scale English-toFrench task show that our method achieves better translation performance by 1 BLEU point over the large vocabulary neural machine translation system of Jean et al. (2015).

Connected through hyperedge HE-1 (induced by source paper "sequence generation from both sides to the middle"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "accelerating neural transformer via an average attention network"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
    - "parallel wavenet fast high fidelity speech synthesis"

Connected through hyperedge HE-2 (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"
    - "neural machine translation decoding with terminology constraints"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "on learning meaningful code changes via neural machine translation"):
  with members:
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "semantically enhanced software traceability using deep learning techniques"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "massive exploration of neural machine translation architectures"
    - "learn fuzz machine learning for input fuzzing"
    - "deepsoft a vision for a deep model of software"
    - "a deep learning model for estimating story points"
    - "deep api learning"

Connected through hyperedge HE-4 (induced by source paper "graph based bilingual word embedding for statistical machine translation"):
  with members:
    - "syntax directed attention for neural machine translation"
    - "mimicking word embeddings using subword rnns"
    - "chunk based bi scale decoder for neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "six challenges for neural machine translation"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "cross lingual models of word embeddings an empirical comparison"

Connected through hyperedge HE-5 (induced by source paper "neural based pinyin to character conversion with adaptive vocabulary"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "effective character augmented word embedding for machine reading comprehension"
    - "subword augmented embedding for cloze reading comprehension"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "character word lstm language models"
    - "vocabulary selection strategies for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "phrase table as recommendation memory for neural machine translation"):
  with members:
    - "translating phrases in neural machine translation"
    - "memory augmented neural machine translation"
    - "neural machine translation advised by statistical machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "bridging neural machine translation and bilingual dictionaries"
    - "a coverage embedding model for neural machine translation"
    - "the amu uedin submission to the wmt16 news translation task attention based nmt models as feature functions in phrase based smt"

Connected through hyperedge HE-7 (induced by source paper "learning to compute word embeddings on the fly"):
  with members:
    - "dynamic integration of background knowledge in neural nlu systems"
    - "vocabulary selection strategies for neural machine translation"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a comparative study of word embeddings for reading comprehension"
    - "leveraging lexical resources for learning entity embeddings in multi relational data"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "squad 100 000 questions for machine comprehension of text"
    - "dynamic coattention networks for question answering"

Connected through hyperedge HE-8 (induced by source paper "speeding up neural machine translation decoding by cube pruning"):
  with members:
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "coverage embedding models for neural machine translation"
    - "a convolutional encoder model for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "on using very large target vocabulary for neural machine translation"
    - "sequence to sequence learning with neural networks"

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)