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 "multimodal differential network for visual question generation"):
  Abstract: Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).

Includes members:
  Member "differential attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust explanations for visual question answering")
  Member "learning to ask neural question generation for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "creativity generating diverse questions using variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "what s in a question using visual questions as a form of supervision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep bayesian network for visual question generation")
  Member "end to end optimization of goal driven and visually grounded dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised question decomposition for question answering")
  Member "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")
  Member "guesswhat visual object discovery through multi modal dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "hierarchical question image co attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)