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 "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"):
  Abstract: Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc. This is largely due to the release of pre-trained contextualized representations such as BERT and ELMo, which can be fine-tuned for the target task. Despite those advances and the creation of more challenging datasets, most of the work is still done for English. Here, we study the effectiveness of multilingual BERT fine-tuned on large-scale English datasets for reading comprehension (e.g., for RACE), and we apply it to Bulgarian multiple-choice reading comprehension. We propose a new dataset containing 2,221 questions from matriculation exams for twelfth grade in various subjects -history, biology, geography and philosophy-, and 412 additional questions from online quizzes in history. While the quiz authors gave no relevant context, we incorporate knowledge from Wikipedia, retrieving documents matching the combination of question + each answer option. Moreover, we experiment with different indexing and pre-training strategies. The evaluation results show accuracy of 42.23%, which is well above the baseline of 24.89%.

Includes members:
  Member "passage re ranking with bert":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "improving question answering with external knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sirenless reveal the intention behind news")
  Member "transformer xl attentive language models beyond a fixed length context":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "dream a challenge dataset and models for dialogue based reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")
  Member "xlnet generalized autoregressive pretraining for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "cross lingual language model pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "massively multilingual neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "can a suit of armor conduct electricity a new dataset for open book question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "natural language qa approaches using reasoning with external knowledge")

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)