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 "structured attentions for visual question answering"):
  Abstract: Visual attention, which assigns weights to image regions according to their relevance to a question, is considered as an indispensable part by most Visual Question Answering models. Although the questions may involve complex relations among multiple regions, few attention models can effectively encode such cross-region relations. In this paper, we demonstrate the importance of encoding such relations by showing the limited effective receptive field of ResNet on two datasets, and propose to model the visual attention as a multivariate distribution over a grid-structured Conditional Random Field on image regions. We demonstrate how to convert the iterative inference algorithms, Mean Field and Loopy Belief Propagation, as recurrent layers of an end-to-end neural network. We empirically evaluated our model on 3 datasets, in which it surpasses the best baseline model of the newly released CLEVR dataset by 9.5%, and the best published model on the VQA dataset by 1.25%. Source code is available at https: //github.com/zhuchen03/vqa-sva.

Includes members:
  Member "structured attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks")
  Member "visual question answering datasets algorithms and future challenges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a question centric model for visual question answering in medical imaging")
  Member "understanding the effective receptive field in deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos")
  Member "hadamard product for low rank bilinear pooling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "component analysis for visual question answering architectures")
  Member "training recurrent answering units with joint loss minimization for vqa":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cq vqa visual question answering on categorized questions")
  Member "dual attention networks for multimodal reasoning and matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vsgnet spatial attention network for detecting human object interactions using graph convolutions")
  Member "a focused dynamic attention model for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multimodal unified attention networks for vision and language interactions")
  Member "multimodal residual learning for visual qa":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anatomy aware 3d human pose estimation in videos")

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)