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: "question relevance in vqa identifying non visual and false premise questions"
  Abstract: Visual Question Answering (VQA) is the task of answering natural-language questions about images. We introduce the novel problem of determining the relevance of questions to images in VQA. Current VQA models do not reason about whether a question is even related to the given image (e.g. What is the capital of Argentina?) or if it requires information from external resources to answer correctly. This can break the continuity of a dialogue in human-machine interaction. Our approaches for determining relevance are composed of two stages. Given an image and a question, (1) we first determine whether the question is visual or not, (2) if visual, we determine whether the question is relevant to the given image or not. Our approaches, based on LSTM-RNNs, VQA model uncertainty, and caption-question similarity, are able to outperform strong baselines on both relevance tasks. We also present human studies showing that VQA models augmented with such question relevance reasoning are perceived as more intelligent, reasonable, and human-like.

Connected through hyperedge HE-1 (induced by source paper "clevr ref diagnosing visual reasoning with referring expressions"):
  with members:
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "mattnet modular attention network for referring expression comprehension"
    - "visual referring expression recognition what do systems actually learn"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"

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"
    - "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-3 (induced by source paper "generating natural language explanations for visual question answering using scene graphs and visual attention"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "attentive explanations justifying decisions and pointing to the evidence extended abstract"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "learning to act properly predicting and explaining affordances from images"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "generating visual explanations"
    - "exploring the limits of language modeling"

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"
    - "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"

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"
    - "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"
    - "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 "do explanations make vqa models more predictable to a human"):
  with members:
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "towards transparent ai systems interpreting visual question answering models"
    - "learning language games through interaction"
    - "analyzing the behavior of visual question answering models"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "hierarchical question image co attention for visual question answering"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"

Connected through hyperedge HE-7 (induced by source paper "question relevance in visual question answering"):
  with members:
    - "the promise of premise harnessing question premises in visual question answering"
    - "xgboost a scalable tree boosting system"
    - "spice semantic propositional image caption evaluation"
    - "enriching word vectors with subword information"
    - "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-8 (induced by source paper "it takes two to tango towards theory of ai s mind"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "hadamard product for low rank bilinear pooling"
    - "attentive explanations justifying decisions and pointing to the evidence"
    - "who is mistaken"
    - "towards transparent ai systems interpreting visual question answering models"
    - "learning language games through interaction"
    - "analyzing the behavior of visual question answering models"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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)