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: "marian fast neural machine translation in c"
  Abstract: We present Marian, an efficient and self-contained Neural Machine Translation framework with an integrated automatic differentiation engine based on dynamic computation graphs. Marian is written entirely in C++. We describe the design of the encoder-decoder framework and demonstrate that a research-friendly toolkit can achieve high training and translation speed.

Connected through hyperedge HE-1 (induced by source paper "context in neural machine translation a review of models and evaluations"):
  with members:
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "assessing crosslingual discourse relations in machine translation"
    - "pronoun translation in english french machine translation an analysis of error types"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "automatic reference based evaluation of pronoun translation misses the point"
    - "context aware neural machine translation learns anaphora resolution"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"
    - "a large scale test set for the evaluation of context aware pronoun translation in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "hybrid data model parallel training for sequence to sequence recurrent neural network machine translation"):
  with members:
    - "scaling neural machine translation"
    - "unifying data model and hybrid parallelism in deep learning via tensor tiling"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "massive exploration of neural machine translation architectures"
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-3 (induced by source paper "domain translationese and noise in synthetic data for neural machine translation"):
  with members:
    - "tagged back translation"
    - "the effect of translationese in machine translation test sets"
    - "using monolingual data in neural machine translation a systematic study"
    - "the source target domain mismatch problem in machine translation"
    - "ape at scale and its implications on mt evaluation biases"
    - "on the evaluation of machine translation systems trained with back translation"
    - "translationese in machine translation evaluation"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-4 (induced by source paper "a simple and effective approach to automatic post editing with transfer learning"):
  with members:
    - "unbabel s submission to the wmt2019 ape shared task bert based encoder decoder for automatic post editing"
    - "cross lingual language model pretraining"
    - "escape a large scale synthetic corpus for automatic post editing"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-5 (induced by source paper "translator2vec understanding and representing human post editors"):
  with members:
    - "recurrent neural network based user authentication for freely typed keystroke data"
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "learning to represent edits"
    - "attention is all you need"
    - "a practical approach to dialogue response generation in closed domains"
    - "scalable semantic matching of queries to ads in sponsored search advertising"
    - "bag of tricks for efficient text classification"
    - "a convolutional encoder model for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "diabla a corpus of bilingual spontaneous written dialogues for machine translation"):
  with members:
    - "a challenge set approach to evaluating machine translation"
    - "neural machine translation with extended context"
    - "evaluating discourse phenomena in neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "english czech systems in wmt19 document level transformer"):
  with members:
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "a call for clarity in reporting bleu scores"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "a large scale comparison of historical text normalization systems"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "evaluating historical text normalization systems how well do they generalize"
    - "improving historical spelling normalization with bi directional lstms and multi task learning"
    - "an open diachronic corpus of historical spanish annotation criteria and automatic modernisation of spelling"

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)