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: "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
  Abstract: Rich and dense human labeled datasets are among the main enabling factors for the recent advance on vision-language understanding. Many seemingly distant annotations (e.g., semantic segmentation and visual question answering (VQA)) are inherently connected in that they reveal different levels and perspectives of human understandings about the same visual scenes --- and even the same set of images (e.g., of COCO). The popularity of COCO correlates those annotations and tasks. Explicitly linking them up may significantly benefit both individual tasks and the unified vision and language modeling. We present the preliminary work of linking the instance segmentations provided by COCO to the questions and answers (QAs) in the VQA dataset, and name the collected links visual questions and segmentation answers (VQS). They transfer human supervision between the previously separate tasks, offer more effective leverage to existing problems, and also open the door for new research problems and models. We study two applications of the VQS data in this paper: supervised attention for VQA and a novel question-focused semantic segmentation task. For the former, we obtain state-of-the-art results on the VQA real multiple-choice task by simply augmenting the multilayer perceptrons with some attention features that are learned using the segmentation-QA links as explicit supervision. To put the latter in perspective, we study two plausible methods and compare them to an oracle method assuming that the instance segmentations are given at the test stage.

Connected through hyperedge HE-1 (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"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"

Connected through hyperedge HE-3 (induced by source paper "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"):
  with members:
    - "word learning and the acquisition of syntactic semantic overhypotheses"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "object level visual reasoning in videos"

Connected through hyperedge HE-4 (induced by source paper "compact trilinear interaction for visual question answering"):
  with members:
    - "question type guided attention in visual question answering"
    - "bilinear attention networks"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "visual question answering as a meta learning task"
    - "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"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"

Connected through hyperedge HE-5 (induced by source paper "depthwise convolution is all you need for learning multiple visual domains"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "a survey on methods and theories of quantized neural networks"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "network dissection quantifying interpretability of deep visual representations"
    - "transitive invariance for self supervised visual representation learning"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "incremental learning through deep adaptation"

Connected through hyperedge HE-6 (induced by source paper "improving question answering over incomplete kbs with knowledge aware reader"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "language conditioned graph networks for relational reasoning"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "a graph to sequence model for amr to text generation"

Connected through hyperedge HE-7 (induced by source paper "neural symbolic vqa disentangling reasoning from vision and language understanding"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "visual question reasoning on general dependency tree"
    - "inferring and executing programs for visual reasoning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-8 (induced by source paper "faithful multimodal explanation for visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention is not explanation"
    - "self critical reasoning for robust visual question answering"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "dynamic filtering with large sampling field for convnets"
    - "constituency parsing with a self attentive encoder"
    - "joint image captioning and 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)