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 "learning to skim text"):
  Abstract: Recurrent Neural Networks are showing much promise in many sub-areas of natural language processing, ranging from document classification to machine translation to automatic question answering. Despite their promise, many recurrent models have to read the whole text word by word, making it slow to handle long documents. For example, it is difficult to use a recurrent network to read a book and answer questions about it. In this paper, we present an approach of reading text while skipping irrelevant information if needed. The underlying model is a recurrent network that learns how far to jump after reading a few words of the input text. We employ a standard policy gradient method to train the model to make discrete jumping decisions. In our benchmarks on four different tasks, including number prediction, sentiment analysis, news article classification and automatic Q\&A, our proposed model, a modified LSTM with jumping, is up to 6 times faster than the standard sequential LSTM, while maintaining the same or even better accuracy.

Includes members:
  Member "learning recurrent span representations for extractive question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "reasonet learning to stop reading in machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "description based text classification with reinforcement learning")
  Member "a thorough examination of the cnn daily mail reading comprehension task":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "a parallel hierarchical model for machine comprehension on sparse data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "end to end answer chunk extraction and ranking for reading comprehension")
  Member "neural symbolic machines learning semantic parsers on freebase with weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "adaptive computation time for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention over parameters for dialogue systems")
  Member "abstractive text summarization using sequence to sequence rnns and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "bidirectional attention flow for machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "description based text classification with reinforcement learning")

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)