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 "title guided encoding for keyphrase generation"):
  Abstract: Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoder-decoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a title-guided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-of-the-art models with a large margin, especially for documents with either very low or very high title length ratios.

Includes members:
  Member "keyphrase generation with correlation constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "learning feature representations for keyphrase extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a review of keyphrase extraction")
  Member "generating distractors for reading comprehension questions from real examinations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "good better best textual distractors generation for multi choice vqa via policy gradient")
  Member "semi supervised learning for neural keyphrase generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "a unified query based generative model for question generation and question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "opennmt open source toolkit for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "deep keyphrase generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "two huge title and keyword generation corpora of research articles")
  Member "a joint model for question answering and question generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")

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)