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 "difficulty controllable question generation for reading comprehension"):
  Abstract: We investigate the difficulty levels of questions, and propose a new setting called Difficulty-controllable Question Generation (DQG). Taking as input a reading comprehension paragraph and some text fragments (i.e. answers) in the paragraph that we want to ask questions about, a DQG method needs to generate questions each of which has a given text fragment as its answer, and meanwhile the generation is under the control of specified difficulty labels---the output questions should satisfy the specified difficulty as much as possible. To solve this task, we propose an end-to-end framework to generate questions of designated difficulty levels. Specifically, we explore a few intuitions: (i) In the input sentences, the nearer a word is to the answer fragment, the more likely it is used in the question; (ii) The easier a question is, the nearer its words are to the answer fragment in the sentence; (iii) Performing difficulty control could be regarded as a problem of sentence generation towards a specified attribute or style, namely difficulty level. For evaluation, we prepared the first dataset of reading comprehension questions with difficulty labels. The results show that our framework not only generates questions of better quality under the metrics like BLEU, but also has the capability to generate questions complying with the specified difficulty labels.

Includes members:
  Member "generating distractors for reading comprehension questions from real examinations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "good better best textual distractors generation for multi choice vqa via policy gradient")
  Member "gaan gated attention networks for learning on large and spatiotemporal graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "harvesting paragraph level question answer pairs from wikipedia":
    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 "title guided encoding for keyphrase generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "keyphrase generation a multi aspect survey")
  Member "style transfer from non parallel text by cross alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "learning to ask neural question generation for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "race large scale reading comprehension dataset from examinations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")
  Member "get to the point summarization with pointer generator networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mast a memory augmented self supervised tracker")

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)