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: "tell and answer towards explainable visual question answering using attributes and captions"
  Abstract: Visual Question Answering (VQA) has attracted attention from both computer vision and natural language processing communities. Most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer. Although such end-to-end models might report promising performance, they rarely provide any insight, apart from the answer, into the VQA process. In this work, we propose to break up the end-to-end VQA into two steps: explaining and reasoning, in an attempt towards a more explainable VQA by shedding light on the intermediate results between these two steps. To that end, we first extract attributes and generate descriptions as explanations for an image using pre-trained attribute detectors and image captioning models, respectively. Next, a reasoning module utilizes these explanations in place of the image to infer an answer to the question. The advantages of such a breakdown include: (1) the attributes and captions can reflect what the system extracts from the image, thus can provide some explanations for the predicted answer; (2) these intermediate results can help us identify the inabilities of both the image understanding part and the answer inference part when the predicted answer is wrong. We conduct extensive experiments on a popular VQA dataset and dissect all results according to several measurements of the explanation quality. Our system achieves comparable performance with the state-of-the-art, yet with added benefits of explainability and the inherent ability to further improve with higher quality explanations.

Connected through hyperedge HE-1 (induced by source paper "relation aware graph attention network for visual question answering"):
  with members:
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "learning visual question answering by bootstrapping hard attention"
    - "learning to count objects in natural images for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "differential attention for visual question answering"
    - "learning visual knowledge memory networks for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"

Connected through hyperedge HE-2 (induced by source paper "a fast and accurate one stage approach to visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "neural sequential phrase grounding seqground"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "yolov3 an incremental improvement"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "unsupervised image captioning"
    - "mattnet modular attention network for referring expression comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "generating question relevant captions to aid visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self critical reasoning for robust visual question answering"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "discriminability objective for training descriptive captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "dynamic filtering with large sampling field for convnets"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "non monotonic logical reasoning guiding deep learning for explainable visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "answering visual what if questions from actions to predicted scene descriptions"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "visual question answering as a meta learning task"

Connected through hyperedge HE-5 (induced by source paper "understand compose and respond answering visual questions by a composition of abstract procedures"):
  with members:
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "visual question answering datasets algorithms and future challenges"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "vqa with no questions answers training"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "understand compose and respond answering visual questions by a composition of abstract procedures"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "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"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-7 (induced by source paper "vqa e explaining elaborating and enhancing your answers for visual questions"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "stack captioning coarse to fine learning for image captioning"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "towards transparent ai systems interpreting visual question answering models"

Connected through hyperedge HE-8 (induced by source paper "joint image captioning and question answering"):
  with members:
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "attention on attention architectures for visual question answering vqa"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "areas of attention for image captioning"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "diverse beam search decoding diverse solutions from neural sequence models"

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)