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 "automating reading comprehension by generating question and answer pairs"):
  Abstract: Neural network-based methods represent the state-of-the-art in question generation from text. Existing work focuses on generating only questions from text without concerning itself with answer generation. Moreover, our analysis shows that handling rare words and generating the most appropriate question given a candidate answer are still challenges facing existing approaches. We present a novel two-stage process to generate question-answer pairs from the text. For the first stage, we present alternatives for encoding the span of the pivotal answer in the sentence using Pointer Networks. In our second stage, we employ sequence to sequence models for question generation, enhanced with rich linguistic features. Finally, global attention and answer encoding are used for generating the question most relevant to the answer. We motivate and linguistically analyze the role of each component in our framework and consider compositions of these. This analysis is supported by extensive experimental evaluations. Using standard evaluation metrics as well as human evaluations, our experimental results validate the significant improvement in the quality of questions generated by our framework over the state-of-the-art. The technique presented here represents another step towards more automated reading comprehension assessment. We also present a live system \footnote{Demo of the system is available at \url{this https URL}.} to demonstrate the effectiveness of our approach.

Includes members:
  Member "learning to ask neural question generation for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "tree to sequence attentional neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")
  Member "squad 100 000 questions for machine comprehension of text":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "linguistic input features improve neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "augmenting neural machine translation with knowledge graphs")
  Member "effective approaches to attention based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention over parameters for dialogue systems")
  Member "on the properties of neural machine translation encoder decoder approaches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "neural machine translation by jointly learning to align and translate":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)