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: "learning to translate for multilingual question answering"
  Abstract: In multilingual question answering, either the question needs to be translated into the document language, or vice versa. In addition to direction, there are multiple methods to perform the translation, four of which we explore in this paper: word-based, 10-best, context-based, and grammar-based. We build a feature for each combination of translation direction and method, and train a model that learns optimal feature weights. On a large forum dataset consisting of posts in English, Arabic, and Chinese, our novel learn-to-translate approach was more effective than a strong baseline (p<0.05): translating all text into English, then training a classifier based only on English (original or translated) text.

Connected through hyperedge HE-1 (induced by source paper "xcmrc evaluating cross lingual machine reading comprehension"):
  with members:
    - "a corpus for multilingual document classification in eight languages"
    - "xnli evaluating cross lingual sentence representations"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "multilingual extractive reading comprehension by runtime machine translation"):
  with members:
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "reinforced mnemonic reader for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "making neural qa as simple as possible but not simpler"
    - "dcn mixed objective and deep residual coattention for question answering"
    - "reinforced mnemonic reader for machine reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "cross language learning with adversarial neural networks application to community question answering"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "global thread level inference for comment classification in community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "machine translation evaluation meets community question answering"
    - "cross lingual models of word embeddings an empirical comparison"
    - "unrolled generative adversarial networks"

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)