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: "automatic generation of grounded visual questions"
  Abstract: In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image. Visual question generation is an emerging topic which aims to ask questions in natural language based on visual input. To the best of our knowledge, it lacks automatic methods to generate meaningful questions with various types for the same visual input. To circumvent the problem, we propose a model that automatically generates visually grounded questions with varying types. Our model takes as input both images and the captions generated by a dense caption model, samples the most probable question types, and generates the questions in sequel. The experimental results on two real world datasets show that our model outperforms the strongest baseline in terms of both correctness and diversity with a wide margin.

Connected through hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations"):
  with members:
    - "deep bayesian network for visual question generation"
    - "incorporating visual semantics into sentence representations within a grounded space"
    - "visualizing and measuring the geometry of bert"
    - "lxmert learning cross modality encoder representations from transformers"
    - "bert rediscovers the classical nlp pipeline"
    - "context aware zero shot learning for object recognition"
    - "fusion of detected objects in text for visual question answering"
    - "supervised multimodal bitransformers for classifying images and text"

Connected through hyperedge HE-2 (induced by source paper "information maximizing visual question generation"):
  with members:
    - "neural baby talk"
    - "fast decoding in sequence models using discrete latent variables"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "learning to ask neural question generation for reading comprehension"
    - "inferring and executing programs for visual reasoning"
    - "visual question generation as dual task of visual question answering"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-3 (induced by source paper "recent advances in neural question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a better metric for evaluating question generation systems"
    - "improving neural question generation using answer separation"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"

Connected through hyperedge HE-4 (induced by source paper "gqa a new dataset for real world visual reasoning and compositional question answering"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "compositional attention networks for machine reasoning"
    - "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"
    - "scene graph generation by iterative message passing"

Connected through hyperedge HE-5 (induced by source paper "inverse visual question answering with multi level attentions"):
  with members:
    - "inverse visual question answering a new benchmark and vqa diagnosis tool"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "ivqa inverse visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "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"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-6 (induced by source paper "sunny and dark outside improving answer consistency in vqa through entailed question generation"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "cycle consistency for robust visual question answering"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "evaluating quality of chatbots and intelligent conversational agents"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "learning by asking questions"
    - "the promise of premise harnessing question premises in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-7 (induced by source paper "a free lunch in generating datasets building a vqg and vqa system with attention and humans in the loop"):
  with members:
    - "attention is all you need"
    - "learning to learn from noisy web videos"
    - "analyzing the behavior of visual question answering models"
    - "generating natural questions about an image"
    - "visual question answering a survey of methods and datasets"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "vqa visual question answering"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "weak supervision enhanced generative network for question generation"):
  with members:
    - "neural models for key phrase detection and question generation"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"
    - "neural question generation from text a preliminary study"
    - "question asking as program generation"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "language modeling with gated convolutional networks"

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)