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: "ivqa inverse visual question answering"
  Abstract: We propose the inverse problem of Visual question answering (iVQA), and explore its suitability as a benchmark for visuo-linguistic understanding. The iVQA task is to generate a question that corresponds to a given image and answer pair. Since the answers are less informative than the questions, and the questions have less learnable bias, an iVQA model needs to better understand the image to be successful than a VQA model. We pose question generation as a multi-modal dynamic inference process and propose an iVQA model that can gradually adjust its focus of attention guided by both a partially generated question and the answer. For evaluation, apart from existing linguistic metrics, we propose a new ranking metric. This metric compares the ground truth question's rank among a list of distractors, which allows the drawbacks of different algorithms and sources of error to be studied. Experimental results show that our model can generate diverse, grammatically correct and content correlated questions that match the given answer.

Connected through hyperedge HE-1 (induced by source paper "focal visual text attention for memex question answering"):
  with members:
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "differential attention for visual question answering"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-2 (induced by source paper "cycle consistency for robust visual question answering"):
  with members:
    - "bilinear attention networks"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "the natural language decathlon multitask learning as question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-3 (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"
    - "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"
    - "image captioning with semantic attention"

Connected through hyperedge HE-4 (induced by source paper "good better best textual distractors generation for multi choice vqa via policy gradient"):
  with members:
    - "augmenting model robustness with transformation invariant attacks"
    - "from recognition to cognition visual commonsense reasoning"
    - "generating natural language adversarial examples"
    - "generating distractors for reading comprehension questions from real examinations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "which training methods for gans do actually converge"
    - "darts deceiving autonomous cars with toxic signs"
    - "dancin seq2seq fooling text classifiers with adversarial text example generation"

Connected through hyperedge HE-5 (induced by source paper "inverse visual question answering a new benchmark and vqa diagnosis tool"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning to reason end to end module networks for visual question answering"
    - "the promise of premise harnessing question premises in visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "active learning for visual question answering an empirical study"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-6 (induced by source paper "composing text and image for image retrieval an empirical odyssey"):
  with members:
    - "attributes as operators"
    - "mattnet modular attention network for referring expression comprehension"
    - "dynamic few shot visual learning without forgetting"
    - "dialog based interactive image retrieval"
    - "in defense of the triplet loss for person re identification"
    - "language based image editing with recurrent attentive models"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "learning by asking questions"):
  with members:
    - "deep reinforcement learning that matters"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "a joint model for question answering and question generation"
    - "question asking as program generation"
    - "film visual reasoning with a general conditioning layer"
    - "semi supervised qa with generative domain adaptive nets"

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)