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 "an analysis of visual question answering algorithms"):
  Abstract: In visual question answering (VQA), an algorithm must answer text-based questions about images. While multiple datasets for VQA have been created since late 2014, they all have flaws in both their content and the way algorithms are evaluated on them. As a result, evaluation scores are inflated and predominantly determined by answering easier questions, making it difficult to compare different methods. In this paper, we analyze existing VQA algorithms using a new dataset. It contains over 1.6 million questions organized into 12 different categories. We also introduce questions that are meaningless for a given image to force a VQA system to reason about image content. We propose new evaluation schemes that compensate for over-represented question-types and make it easier to study the strengths and weaknesses of algorithms. We analyze the performance of both baseline and state-of-the-art VQA models, including multi-modal compact bilinear pooling (MCB), neural module networks, and recurrent answering units. Our experiments establish how attention helps certain categories more than others, determine which models work better than others, and explain how simple models (e.g. MLP) can surpass more complex models (MCB) by simply learning to answer large, easy question categories.

Includes members:
  Member "visual question answering datasets algorithms and future challenges":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a question centric model for visual question answering in medical imaging")
  Member "training recurrent answering units with joint loss minimization for vqa":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cq vqa visual question answering on categorized questions")
  Member "a focused dynamic attention model for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multimodal unified attention networks for vision and language interactions")
  Member "dualnet domain invariant network for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "figure captioning with reasoning and sequence level training")
  Member "multimodal residual learning for visual qa":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anatomy aware 3d human pose estimation in videos")
  Member "visual genome connecting language and vision using crowdsourced dense image annotations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "learning to compose neural networks for question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised question decomposition for question answering")
  Member "clevr a diagnostic dataset for compositional language and elementary visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)