human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "difficulty controllable generation of reading comprehension questions"
  Abstract: We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG). Taking as input a sentence in the reading comprehension paragraph and some of its text fragments (i.e., answers) 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 by exploring a few important intuitions. For evaluation, we prepared the first dataset of reading comprehension questions with difficulty labels. The results show that the question generated by our framework not only have better quality under the metrics like BLEU, but also comply with the specified difficulty labels.

Connected through hyperedge HE-1 (induced by source paper "learning to generate questions by learningwhat not to generate"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multiresolution graph attention networks for relevance matching"
    - "difficulty controllable question generation for reading comprehension"
    - "improving neural question generation using answer separation"
    - "relational inductive biases deep learning and graph networks"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "sequential copying networks"
    - "neural models for key phrase detection and question generation"

Connected through hyperedge HE-2 (induced by source paper "semi supervised text style transfer cross projection in latent space"):
  with members:
    - "imat unsupervised text attribute transfer via iterative matching and translation"
    - "style transfer as unsupervised machine translation"
    - "disentangled representation learning for non parallel text style transfer"
    - "unsupervised stylish image description generation via domain layer norm"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "language style transfer from sentences with arbitrary unknown styles"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"

Connected through hyperedge HE-3 (induced by source paper "interconnected question generation with coreference alignment and conversation flow modeling"):
  with members:
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "generating distractors for reading comprehension questions from real examinations"
    - "coqa a conversational question answering challenge"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"
    - "neural question generation from text a preliminary study"
    - "get to the point summarization with pointer generator networks"
    - "machine comprehension by text to text neural question generation"

Connected through hyperedge HE-4 (induced by source paper "review based question generation with adaptive instance transfer and augmentation"):
  with members:
    - "answerer in questioner s mind information theoretic approach to goal oriented visual dialog"
    - "dual ask answer network for machine reading comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"
    - "two stage synthesis networks for transfer learning in machine comprehension"
    - "neural question generation from text a preliminary study"

Connected through hyperedge HE-5 (induced by source paper "improving question generation with to the point context"):
  with members:
    - "improving neural question generation using answer separation"
    - "generating distractors for reading comprehension questions from real examinations"
    - "learning to ask neural question generation for reading comprehension"
    - "neural question generation from text a preliminary study"
    - "get to the point summarization with pointer generator networks"
    - "faithful to the original fact aware neural abstractive summarization"
    - "machine comprehension by text to text neural question generation"
    - "pointing the unknown words"

Question: Please tell me which class the center node 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)