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 "one shot learning for question answering in gaokao history challenge"):
  Abstract: Answering questions from university admission exams (Gaokao in Chinese) is a challenging AI task since it requires effective representation to capture complicated semantic relations between questions and answers. In this work, we propose a hybrid neural model for deep question-answering task from history examinations. Our model employs a cooperative gated neural network to retrieve answers with the assistance of extra labels given by a neural turing machine labeler. Empirical study shows that the labeler works well with only a small training dataset and the gated mechanism is good at fetching the semantic representation of lengthy answers. Experiments on question answering demonstrate the proposed model obtains substantial performance gains over various neural model baselines in terms of multiple evaluation metrics.

Includes members:
  Member "a question focused multi factor attention network for question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effective modeling of encoder decoder architecture for joint entity and relation extraction")
  Member "subword augmented embedding for cloze reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sg net syntax guided machine reading comprehension")
  Member "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effective subword segmentation for text comprehension")
  Member "modeling multi turn conversation with deep utterance aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sequential neural networks for noetic end to end response selection")
  Member "semi supervised qa with generative domain adaptive nets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "description based text classification with reinforcement learning")
  Member "bilateral multi perspective matching for natural language sentences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "answering complex questions using open information extraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "question answering via integer programming over semi structured knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reasoning driven question answering for natural language understanding")

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)