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 promise of premise harnessing question premises in visual question answering"
  Abstract: In this paper, we make a simple observation that questions about images often contain premises - objects and relationships implied by the question - and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented with a question that is irrelevant to an image, state-of-the-art VQA models will still answer purely based on learned language biases, resulting in non-sensical or even misleading answers. We note that a visual question is irrelevant to an image if at least one of its premises is false (i.e. not depicted in the image). We leverage this observation to construct a dataset for Question Relevance Prediction and Explanation (QRPE) by searching for false premises. We train novel question relevance detection models and show that models that reason about premises consistently outperform models that do not. We also find that forcing standard VQA models to reason about premises during training can lead to improvements on tasks requiring compositional reasoning.

Connected through hyperedge HE-1 (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-2 (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"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "fooling vision and language models despite localization and attention mechanism"

Connected through hyperedge HE-3 (induced by source paper "why does a visual question have different answers"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "crowdsourcing ground truth for medical relation extraction"
    - "an analysis of visual question answering algorithms"
    - "learning to disambiguate by asking discriminative questions"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "crowdsourcing question answer meaning representations"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "predicting foreground object ambiguity and efficiently crowdsourcing the segmentation s"

Connected through hyperedge HE-4 (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"
    - "ivqa inverse visual question answering"
    - "learning to reason end to end module networks for 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-5 (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"
    - "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"
    - "visual7w grounded question answering in images"

Connected through hyperedge HE-6 (induced by source paper "question relevance in visual question answering"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "spice semantic propositional image caption evaluation"
    - "enriching word vectors with subword information"
    - "question relevance in vqa identifying non visual and false premise questions"
    - "multi cue zero shot learning with strong supervision"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "vqa visual question answering"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "deep learning evaluation using deep linguistic processing"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "shapeworld a new test methodology for multimodal language understanding"
    - "film visual reasoning with a general conditioning layer"
    - "adversarial examples for evaluating reading comprehension systems"

Connected through hyperedge HE-8 (induced by source paper "active learning for visual question answering an empirical study"):
  with members:
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "deep bayesian active learning with image data"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "leveraging visual question answering for image caption ranking"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "hierarchical question image co attention 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)