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 source target domain mismatch problem in machine translation"
  Abstract: While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in. As a result, people often talk about different things in different parts of the world. In this work we study the effect of local context in machine translation and postulate that particularly in low resource settings this causes the domains of the source and target language to greatly mismatch, as the two languages are often spoken in further apart regions of the world with more distinctive cultural traits and unrelated local events. In this work we first propose a controlled setting to carefully analyze the source-target domain mismatch, and its dependence on the amount of parallel and monolingual data. Second, we test both a model trained with back-translation and one trained with self-training. The latter leverages in-domain source monolingual data but uses potentially incorrect target references. We found that these two approaches are often complementary to each other. For instance, on a low-resource Nepali-English dataset the combined approach improves upon the baseline using just parallel data by 2.5 BLEU points, and by 0.6 BLEU point when compared to back-translation.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "facebook ai s wat19 myanmar english translation task submission"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "tagged back translation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "revisiting self training for neural sequence generation"
    - "roberta a robustly optimized bert pretraining approach"
    - "achieving human parity on automatic chinese to english news 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)