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 "question guided hybrid convolution for visual question answering"):
  Abstract: In this paper, we propose a novel Question-Guided Hybrid Convolution (QGHC) network for Visual Question Answering (VQA). Most state-of-the-art VQA methods fuse the high-level textual and visual features from the neural network and abandon the visual spatial information when learning multi-modal features.To address these problems, question-guided kernels generated from the input question are designed to convolute with visual features for capturing the textual and visual relationship in the early stage. The question-guided convolution can tightly couple the textual and visual information but also introduce more parameters when learning kernels. We apply the group convolution, which consists of question-independent kernels and question-dependent kernels, to reduce the parameter size and alleviate over-fitting. The hybrid convolution can generate discriminative multi-modal features with fewer parameters. The proposed approach is also complementary to existing bilinear pooling fusion and attention based VQA methods. By integrating with them, our method could further boost the performance. Extensive experiments on public VQA datasets validate the effectiveness of QGHC.

Includes members:
  Member "r vqa learning visual relation facts with semantic attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "avr attention based salient visual relationship detection")
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "modulating early visual processing by language":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "inferring and executing programs for visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised question decomposition for question answering")
  Member "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interactive natural language based person search")
  Member "mutan multimodal tucker fusion for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "component analysis for visual question answering architectures")
  Member "learning to reason end to end module networks for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")
  Member "film visual reasoning with a general conditioning layer":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "in defense of grid features for visual question answering")

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)