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 question generation as dual task of visual question answering"
  Abstract: Recently visual question answering (VQA) and visual question generation (VQG) are two trending topics in the computer vision, which have been explored separately. In this work, we propose an end-to-end unified framework, the Invertible Question Answering Network (iQAN), to leverage the complementary relations between questions and answers in images by jointly training the model on VQA and VQG tasks. Corresponding parameter sharing scheme and regular terms are proposed as constraints to explicitly leverage Q,A's dependencies to guide the training process. After training, iQAN can take either question or answer as input, then output the counterpart. Evaluated on the large-scale visual question answering datasets CLEVR and VQA2, our iQAN improves the VQA accuracy over the baselines. We also show the dual learning framework of iQAN can be generalized to other VQA architectures and consistently improve the results over both the VQA and VQG tasks.

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 "reinforcement learning based graph to sequence model for natural question generation"):
  with members:
    - "bidirectional attentive memory networks for question answering over knowledge bases"
    - "learning to generate questions by learningwhat not to generate"
    - "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "deep iterative and adaptive learning for graph neural networks"
    - "unified language model pre training for natural language understanding and generation"
    - "sql to text generation with graph to sequence model"
    - "towards a better metric for evaluating question generation systems"

Connected through hyperedge HE-3 (induced by source paper "factor graph attention"):
  with members:
    - "a simple baseline for audio visual scene aware dialog"
    - "diverse and coherent paragraph generation from images"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"

Connected through hyperedge HE-4 (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"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "addressing semantic drift in question generation for semi supervised question answering"):
  with members:
    - "synthetic qa corpora generation with roundtrip consistency"
    - "learning to generate questions by learningwhat not to generate"
    - "evaluating rewards for question generation models"
    - "unified language model pre training for natural language understanding and generation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "towards a better metric for evaluating question generation systems"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-6 (induced by source paper "expressing visual relationships via language"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "learning to globally edit images with textual description"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "learning to color from language"
    - "pragmatically informative image captioning with character level reference"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-7 (induced by source paper "video question generation via cross modal self attention networks learning"):
  with members:
    - "lifelong learning for image captioning by asking natural language questions"
    - "end to end dense video captioning with masked transformer"
    - "tvqa localized compositional video question answering"
    - "from film to video multi turn question answering with multi modal context"
    - "learning to ask neural question generation for reading comprehension"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "obj2text generating visually descriptive language from object layouts"

Connected through hyperedge HE-8 (induced by source paper "disentangling pose from appearance in monochrome hand images"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "disentangling latent hands for image synthesis and pose estimation"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "exploring disentangled feature representation beyond face identification"
    - "image to image translation for cross domain disentanglement"
    - "vip cnn visual phrase guided convolutional neural network"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

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)