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 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.

Connected through hyperedge HE-1 (induced by source paper "asking questions the human way scalable question answer generation from text corpus"):
  with members:
    - "addressing semantic drift in question generation for semi supervised question answering"
    - "generating question answer hierarchies"
    - "improving neural question generation using world knowledge"
    - "the curious case of neural text degeneration"
    - "learning to generate questions by learningwhat not to generate"
    - "question type driven question generation"
    - "multi task learning with language modeling for question generation"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "generating question answer hierarchies"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "learning to write with cooperative discriminators"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "know what you don t know unanswerable questions for squad"
    - "improving neural question generation using answer separation"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (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"
    - "improving neural question generation using answer separation"
    - "relational inductive biases deep learning and graph networks"
    - "difficulty controllable generation of reading comprehension questions"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "sequential copying networks"
    - "neural models for key phrase detection and question generation"

Connected through hyperedge HE-4 (induced by source paper "improving neural question generation using world knowledge"):
  with members:
    - "improving neural question generation using answer separation"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"
    - "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"
    - "pointing the unknown words"
    - "newsqa a machine comprehension dataset"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-5 (induced by source paper "star gcn stacked and reconstructed graph convolutional networks for recommender systems"):
  with members:
    - "machine learning for spatiotemporal sequence forecasting a survey"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to rank using localized geometric mean metrics"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"
    - "neural network matrix factorization"

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)