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 "tell and answer towards explainable visual question answering using attributes and captions"):
  Abstract: Visual Question Answering (VQA) has attracted attention from both computer vision and natural language processing communities. Most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer. Although such end-to-end models might report promising performance, they rarely provide any insight, apart from the answer, into the VQA process. In this work, we propose to break up the end-to-end VQA into two steps: explaining and reasoning, in an attempt towards a more explainable VQA by shedding light on the intermediate results between these two steps. To that end, we first extract attributes and generate descriptions as explanations for an image using pre-trained attribute detectors and image captioning models, respectively. Next, a reasoning module utilizes these explanations in place of the image to infer an answer to the question. The advantages of such a breakdown include: (1) the attributes and captions can reflect what the system extracts from the image, thus can provide some explanations for the predicted answer; (2) these intermediate results can help us identify the inabilities of both the image understanding part and the answer inference part when the predicted answer is wrong. We conduct extensive experiments on a popular VQA dataset and dissect all results according to several measurements of the explanation quality. Our system achieves comparable performance with the state-of-the-art, yet with added benefits of explainability and the inherent ability to further improve with higher quality explanations.

Includes members:
  Member "dual attention networks for multimodal reasoning and matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vsgnet spatial attention network for detecting human object interactions using graph convolutions")
  Member "image captioning with semantic attention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "analyzing the behavior of visual question answering models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "a focused dynamic attention model for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multimodal unified attention networks for vision and language interactions")
  Member "hierarchical question image co attention for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "dynamic memory networks for visual and textual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "component analysis for visual question answering architectures")
  Member "making the v in vqa matter elevating the role of image understanding in visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "in defense of grid features for visual question answering")
  Member "multimodal compact bilinear pooling for visual question answering and visual grounding":
    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.CV(Computer Vision and Pattern Recognition)