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: "visual madlibs fill in the blank image generation and question answering"
  Abstract: In this paper, we introduce a new dataset consisting of 360,001 focused natural language descriptions for 10,738 images. This dataset, the Visual Madlibs dataset, is collected using automatically produced fill-in-the-blank templates designed to gather targeted descriptions about: people and objects, their appearances, activities, and interactions, as well as inferences about the general scene or its broader context. We provide several analyses of the Visual Madlibs dataset and demonstrate its applicability to two new description generation tasks: focused description generation, and multiple-choice question-answering for images. Experiments using joint-embedding and deep learning methods show promising results on these tasks.

Connected through hyperedge HE-1 (induced by source paper "language features matter effective language representations for vision language tasks"):
  with members:
    - "bilinear attention networks"
    - "stacked cross attention for image text matching"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "deep contextualized word representations"
    - "multilevel language and vision integration for text to clip retrieval"
    - "open vocabulary phrase detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-2 (induced by source paper "straight to the facts learning knowledge base retrieval for factual visual question answering"):
  with members:
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "image captioning and visual question answering based on attributes and external knowledge"
    - "multimodal residual learning for visual qa"

Connected through hyperedge HE-3 (induced by source paper "out of the box reasoning with graph convolution nets for factual visual question answering"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "disjoint multi task learning between heterogeneous human centric tasks"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "continual learning with deep generative replay"
    - "mask r cnn"
    - "convolutional pose machines"
    - "image captioning with semantic attention"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-5 (induced by source paper "tvqa localized compositional video question answering"):
  with members:
    - "object referring in videos with language and human gaze"
    - "mattnet modular attention network for referring expression comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "obj2text generating visually descriptive language from object layouts"
    - "tall temporal activity localization via language query"
    - "localizing moments in video with natural language"
    - "deepstory video story qa by deep embedded memory networks"
    - "a joint speaker listener reinforcer model for referring expressions"

Connected through hyperedge HE-6 (induced by source paper "vizwiz grand challenge answering visual questions from blind people"):
  with members:
    - "show ask attend and answer a strong baseline for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "the promise of premise harnessing question premises in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "question relevance in vqa identifying non visual and false premise questions"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-7 (induced by source paper "creativity generating diverse questions using variational autoencoders"):
  with members:
    - "improved techniques for training gans"
    - "multimodal residual learning for visual qa"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "image captioning and visual question answering based on attributes and their related external knowledge"
    - "hierarchical question image co attention for visual question answering"
    - "generating natural questions about an image"
    - "revisiting visual question answering baselines"
    - "dynamic memory networks for visual and textual question answering"

Connected through hyperedge HE-8 (induced by source paper "neural motifs scene graph parsing with global context"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "an implementation of faster rcnn with study for region sampling"
    - "neural amr sequence to sequence models for parsing and generation"
    - "learning to reason end to end module networks for visual question answering"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "obj2text generating visually descriptive language from object layouts"

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)