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: "multilingual question answering from formatted text applied to conversational agents"
  Abstract: Recent advances in NLP with language models such as BERT, GPT-2, XLNet or XLM, have allowed surpassing human performance on Reading Comprehension tasks on large-scale datasets (e.g. SQuAD), and this opens up many perspectives for Conversational AI. However, task-specific datasets are mostly in English which makes it difficult to acknowledge progress in foreign languages. Fortunately, state-of-the-art models are now being pre-trained on multiple languages (e.g. BERT was released in a multilingual version managing a hundred languages) and are exhibiting ability for zero-shot transfer from English to others languages on XNLI. In this paper, we run experiments that show that multilingual BERT, trained to solve the complex Question Answering task defined in the English SQuAD dataset, is able to achieve the same task in Japanese and French. It even outperforms the best published results of a baseline which explicitly combines an English model for Reading Comprehension and a Machine Translation Model for transfer. We run further tests on crafted cross-lingual QA datasets (context in one language and question in another) to provide intuition on the mechanisms that allow BERT to transfer the task from one language to another. Finally, we introduce our application Kate. Kate is a conversational agent dedicated to HR support for employees that exploits multilingual models to accurately answer to questions, in several languages, directly from information web pages.

Connected through hyperedge HE-1 (induced by source paper "fquad french question answering dataset"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "mlqa evaluating cross lingual extractive question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

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)