human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "the color of the cat is gray 1 million full sentences visual question answering fsvqa"
  Abstract: Visual Question Answering (VQA) task has showcased a new stage of interaction between language and vision, two of the most pivotal components of artificial intelligence. However, it has mostly focused on generating short and repetitive answers, mostly single words, which fall short of rich linguistic capabilities of humans. We introduce Full-Sentence Visual Question Answering (FSVQA) dataset, consisting of nearly 1 million pairs of questions and full-sentence answers for images, built by applying a number of rule-based natural language processing techniques to original VQA dataset and captions in the MS COCO dataset. This poses many additional complexities to conventional VQA task, and we provide a baseline for approaching and evaluating the task, on top of which we invite the research community to build further improvements.

Connected through hyperedge HE-1 (induced by source paper "unsupervised keyword extraction for full sentence vqa"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "visual question generation for class acquisition of unknown objects"
    - "learning to caption images through a lifetime by asking questions"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "show ask attend and answer a strong baseline for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "customized image narrative generation via interactive visual question generation and answering"):
  with members:
    - "visual question answering datasets algorithms and future challenges"
    - "analyzing the behavior of visual question answering models"
    - "hierarchical question image co attention for visual question answering"
    - "generating natural questions about an image"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "densecap fully convolutional localization networks for dense captioning"
    - "fast r cnn"

Connected through hyperedge HE-3 (induced by source paper "making the v in vqa matter elevating the role of image understanding in visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual question answering datasets algorithms and future challenges"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "learning to reason end to end module networks for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"

Question: Please tell me which class the center node 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)