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: "on the evaluation of machine translation systems trained with back translation"
  Abstract: Back-translation is a widely used data augmentation technique which leverages target monolingual data. However, its effectiveness has been challenged since automatic metrics such as BLEU only show significant improvements for test examples where the source itself is a translation, or translationese. This is believed to be due to translationese inputs better matching the back-translated training data. In this work, we show that this conjecture is not empirically supported and that back-translation improves translation quality of both naturally occurring text as well as translationese according to professional human translators. We provide empirical evidence to support the view that back-translation is preferred by humans because it produces more fluent outputs. BLEU cannot capture human preferences because references are translationese when source sentences are natural text. We recommend complementing BLEU with a language model score to measure fluency.

Connected through hyperedge HE-1 (induced by source paper "question answering is a format when is it useful"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "machine reading comprehension a literature review"
    - "latent retrieval for weakly supervised open domain question answering"
    - "mathqa towards interpretable math word problem solving with operation based formalisms"
    - "learning and evaluating general linguistic intelligence"
    - "compositional questions do not necessitate multi hop reasoning"
    - "entity relation extraction as multi turn question answering"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"

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"
    - "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"
    - "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"
    - "the effect of translationese in machine translation test sets"
    - "ape at scale and its implications on mt evaluation biases"
    - "controlling text complexity in neural machine 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:
    - "the effect of translationese in machine translation test sets"
    - "using monolingual data in neural machine translation a systematic study"
    - "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"

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)