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: "thumt an open source toolkit for neural machine translation"
  Abstract: This paper introduces THUMT, an open-source toolkit for neural machine translation (NMT) developed by the Natural Language Processing Group at Tsinghua University. THUMT implements the standard attention-based encoder-decoder framework on top of Theano and supports three training criteria: maximum likelihood estimation, minimum risk training, and semi-supervised training. It features a visualization tool for displaying the relevance between hidden states in neural networks and contextual words, which helps to analyze the internal workings of NMT. Experiments on Chinese-English datasets show that THUMT using minimum risk training significantly outperforms GroundHog, a state-of-the-art toolkit for NMT.

Connected through hyperedge HE-1 (induced by source paper "on the weaknesses of reinforcement learning for neural machine translation"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "a study of reinforcement learning for neural machine translation"
    - "diversity is all you need learning skills without a reward function"
    - "evaluating text gans as language models"
    - "language gans falling short"
    - "parameter space noise for exploration"
    - "hindsight experience replay"

Connected through hyperedge HE-2 (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"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "slim embedding layers for recurrent neural language models"
    - "memory augmented neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "improving back translation with uncertainty based confidence estimation"):
  with members:
    - "openkiwi an open source framework for quality estimation"
    - "investigating backtranslation in neural machine translation"
    - "quantifying uncertainties in natural language processing tasks"
    - "achieving human parity on automatic chinese to english news translation"
    - "confidence modeling for neural semantic parsing"
    - "triangular architecture for rare language translation"
    - "phrase based neural unsupervised machine translation"
    - "modeling uncertainty with hedged instance embedding"

Connected through hyperedge HE-4 (induced by source paper "neuron interaction based representation composition for neural machine translation"):
  with members:
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "exploiting deep representations for neural machine translation"
    - "weighted transformer network for machine translation"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi head attention with disagreement regularization"

Connected through hyperedge HE-5 (induced by source paper "self attention with structural position representations"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "exploiting sentential context for neural machine translation"
    - "towards better modeling hierarchical structure for self attention with ordered neurons"
    - "modeling recurrence for transformer"
    - "semantic neural machine translation using amr"
    - "adaptive attention span in transformers"
    - "exploiting deep representations for neural machine translation"
    - "self attention with relative position representations"

Connected through hyperedge HE-6 (induced by source paper "document level neural machine translation with inter sentence attention"):
  with members:
    - "semantic role labeling with associated memory network"
    - "improving the transformer translation model with document level context"
    - "context aware neural machine translation learns anaphora resolution"
    - "extreme adaptation for personalized neural machine translation"
    - "modeling coherence for neural machine translation with dynamic and topic caches"
    - "neural machine translation with extended context"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "learning to copy for automatic post editing"):
  with members:
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "cross lingual language model pretraining"
    - "escape a large scale synthetic corpus for automatic post editing"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "modeling localness for self attention networks"
    - "a shared attention mechanism for interpretation of neural automatic post editing systems"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-8 (induced by source paper "neutron an implementation of the transformer translation model and its variants"):
  with members:
    - "on the convergence of adam and beyond"
    - "exploiting deep representations for neural machine translation"
    - "accelerating neural transformer via an average attention network"
    - "training deeper neural machine translation models with transparent attention"
    - "dynamic sentence sampling for efficient training of neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"

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)