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: "neural arabic question answering"
  Abstract: This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.

Connected through hyperedge HE-1 (induced by source paper "tydi qa a benchmark for information seeking question answering in typologically diverse languages"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "latent retrieval for weakly supervised open domain question answering"
    - "what question answering can learn from trivia nerds"
    - "question answering is a format when is it useful"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "mlqa evaluating cross lingual extractive question answering"
    - "a bert baseline for the natural questions"

Connected through hyperedge HE-2 (induced by source paper "arabert transformer based model for arabic language understanding"):
  with members:
    - "towards a human like open domain chatbot"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "albert a lite bert for self supervised learning of language representations"
    - "arsentd lev a multi topic corpus for target based sentiment analysis in arabic levantine tweets"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-3 (induced by source paper "mlqa evaluating cross lingual extractive question answering"):
  with members:
    - "cross lingual transfer learning for question answering"
    - "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"
    - "unsupervised question answering by cloze translation"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "xcmrc evaluating cross lingual machine reading comprehension"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "xlda cross lingual data augmentation for natural language inference and question answering"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"

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)