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 "video question generation via cross modal self attention networks learning"):
  Abstract: Video Question Answering (Video QA) is a critical and challenging task in multimedia comprehension. While deep learning based models are extremely capable of representing and understanding videos, these models heavily rely on massive data, which is expensive to label. In this paper, we introduce a novel task for automatically generating questions given a sequence of video frames and the corresponding subtitles from a clip of video to reduce the huge annotation cost. Learning to ask a question based on a video requires the model to comprehend the rich semantics in the scene and the interplay between the vision and the language. To address this, we propose a novel cross-modal self-attention (CMSA) network to aggregate the diverse features from video frames and subtitles. Excitingly, we demonstrate that our proposed model can improve the (strong) baseline from 0.0738 to 0.1374 in BLEU4 score -- more than 0.063 improvement (i.e., 85\% relatively). Most of all, We arguably pave a novel path toward solving the challenging Video QA task and provide detailed analysis which ushers the avenues for future investigations.

Includes members:
  Member "lifelong learning for image captioning by asking natural language questions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visual grounding in video for unsupervised word translation")
  Member "end to end dense video captioning with masked transformer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi modal dense video captioning")
  Member "tvqa localized compositional video question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical conditional relation networks for video question answering")
  Member "from film to video multi turn question answering with multi modal context":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "videonavqa bridging the gap between visual and embodied question answering")
  Member "learning to ask neural question generation for reading comprehension":
    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 "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "visual question generation as dual task of visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.CV(Computer Vision and Pattern Recognition)