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 "joint learning of answer selection and answer summary generation in community question answering"):
  Abstract: Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-of-the-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries.

Includes members:
  Member "semeval 2017 task 3 community question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a pilot study on multiple choice machine reading comprehension for vietnamese texts")
  Member "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep look into neural ranking models for information retrieval")
  Member "joint training of candidate extraction and answer selection for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a multi type multi span network for reading comprehension that requires discrete reasoning")
  Member "duorc towards complex language understanding with paraphrased reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi style generative reading comprehension")
  Member "neural document summarization by jointly learning to score and select sentences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "selective attention encoders by syntactic graph convolutional networks for document summarization")
  Member "wikihow a large scale text summarization dataset":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "joint multitask learning for community question answering using task specific embeddings":
    also connected through other hyperedges:
      (none)
  Member "multi task learning with multi view attention for answer selection and knowledge base question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "knowledge enhanced attentive learning for answer selection in community question answering systems")

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)