human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mlqa evaluating cross lingual extractive question answering"):
  Abstract: Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in languages other than English, making training QA systems in other languages challenging. An alternative to building large monolingual training datasets is to develop cross-lingual systems which can transfer to a target language without requiring training data in that language. In order to develop such systems, it is crucial to invest in high quality multilingual evaluation benchmarks to measure progress. We present MLQA, a multi-way aligned extractive QA evaluation benchmark intended to spur research in this area. MLQA contains QA instances in 7 languages, namely English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. It consists of over 12K QA instances in English and 5K in each other language, with each QA instance being parallel between 4 languages on average. MLQA is built using a novel alignment context strategy on Wikipedia articles, and serves as a cross-lingual extension to existing extractive QA datasets. We evaluate current state-of-the-art cross-lingual representations on MLQA, and also provide machine-translation-based baselines. In all cases, transfer results are shown to be significantly behind training-language performance.

Includes members:
  Member "neural arabic question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tydi qa a benchmark for information seeking question answering in typologically diverse languages")
  Member "cross lingual transfer learning for question answering":
    also connected through other hyperedges:
      (none)
  Member "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual question answering from formatted text applied to conversational agents")
  Member "unsupervised question answering by cloze translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised question decomposition for question answering")
  Member "synthetic qa corpora generation with roundtrip consistency":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "addressing semantic drift in question generation for semi supervised question answering")
  Member "xcmrc evaluating cross lingual machine reading comprehension":
    also connected through other hyperedges:
      (none)
  Member "korquad1 0 korean qa dataset for machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tydi qa a benchmark for information seeking question answering in typologically diverse languages")
  Member "xlda cross lingual data augmentation for natural language inference and question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised cross lingual representation learning at scale")

Question: Please tell me which class the source paper 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)