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: "the effect of translationese in machine translation test sets"
  Abstract: The effect of translationese has been studied in the field of machine translation (MT), mostly with respect to training data. We study in depth the effect of translationese on test data, using the test sets from the last three editions of WMT's news shared task, containing 17 translation directions. We show evidence that (i) the use of translationese in test sets results in inflated human evaluation scores for MT systems; (ii) in some cases system rankings do change and (iii) the impact translationese has on a translation direction is inversely correlated to the translation quality attainable by state-of-the-art MT systems for that direction.

Connected through hyperedge HE-1 (induced by source paper "on the evaluation of machine translation systems trained with back translation"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "tagged back translation"
    - "bertscore evaluating text generation with bert"
    - "text repair model for neural machine translation"
    - "translationese in machine translation evaluation"
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "investigating backtranslation in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "domain translationese and noise in synthetic data for neural machine translation"):
  with members:
    - "tagged back translation"
    - "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"
    - "marian fast neural machine translation in c"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-3 (induced by source paper "translationese as a language in multilingual nmt"):
  with members:
    - "post editese an exacerbated translationese"
    - "tagged back translation"
    - "ape at scale and its implications on mt evaluation biases"
    - "controlling text complexity in neural machine translation"
    - "on the evaluation of machine translation systems trained with back translation"
    - "translationese in machine translation evaluation"
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-4 (induced by source paper "the source target domain mismatch problem in machine translation"):
  with members:
    - "using monolingual data in neural machine translation a systematic study"
    - "on the evaluation of machine translation systems trained with back translation"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a call for clarity in reporting bleu scores"
    - "rapid adaptation of neural machine translation to new languages"
    - "attention is all you need"
    - "bag of tricks for efficient text classification"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-5 (induced by source paper "document sub structure in neural machine translation"):
  with members:
    - "tagged back translation"
    - "selective attention for context aware neural machine translation"
    - "translationese in machine translation evaluation"
    - "improving the transformer translation model with document level context"
    - "context aware neural machine translation learns anaphora resolution"
    - "modeling coherence for neural machine translation with dynamic and topic caches"
    - "attention is all you need"
    - "domain control for neural machine translation"

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)