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: "pronoun translation in english french machine translation an analysis of error types"
  Abstract: Pronouns are a long-standing challenge in machine translation. We present a study of the performance of a range of rule-based, statistical and neural MT systems on pronoun translation based on an extensive manual evaluation using the PROTEST test suite, which enables a fine-grained analysis of different pronoun types and sheds light on the difficulties of the task. We find that the rule-based approaches in our corpus perform poorly as a result of oversimplification, whereas SMT and early NMT systems exhibit significant shortcomings due to a lack of awareness of the functional and referential properties of pronouns. A recent Transformer-based NMT system with cross-sentence context shows very promising results on non-anaphoric pronouns and intra-sentential anaphora, but there is still considerable room for improvement in examples with cross-sentence dependencies.

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"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "marian fast neural machine translation in c"
    - "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 "toward gender inclusive coreference resolution"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "assessing gender bias in machine translation a case study with google translate"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "gender bias in coreference resolution"
    - "automatic reference based evaluation of pronoun translation misses the point"

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)