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: "dualnet domain invariant network for visual question answering"
  Abstract: Visual question answering (VQA) task not only bridges the gap between images and language, but also requires that specific contents within the image are understood as indicated by linguistic context of the question, in order to generate the accurate answers. Thus, it is critical to build an efficient embedding of images and texts. We implement DualNet, which fully takes advantage of discriminative power of both image and textual features by separately performing two operations. Building an ensemble of DualNet further boosts the performance. Contrary to common belief, our method proved effective in both real images and abstract scenes, in spite of significantly different properties of respective domain. Our method was able to outperform previous state-of-the-art methods in real images category even without explicitly employing attention mechanism, and also outperformed our own state-of-the-art method in abstract scenes category, which recently won the first place in VQA Challenge 2016.

Connected through hyperedge HE-1 (induced by source paper "figure captioning with reasoning and sequence level training"):
  with members:
    - "dvqa understanding data visualizations via question answering"
    - "visual question answering datasets algorithms and future challenges"
    - "a simple neural network module for relational reasoning"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "a focused dynamic attention model for visual question answering"
    - "multimodal residual learning for visual qa"
    - "an actor critic algorithm for sequence prediction"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-2 (induced by source paper "from vqa to multimodal cqa adapting visual qa models for community qa tasks"):
  with members:
    - "bilinear attention networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "soft seeded ssl graphs for unsupervised semantic similarity based retrieval"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "an overview of multi task learning in deep neural networks"
    - "a deep learning approach for expert identification in question answering communities"

Connected through hyperedge HE-3 (induced by source paper "adapting visual question answering models for enhancing multimodal community q a platforms"):
  with members:
    - "bilinear attention networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "soft seeded ssl graphs for unsupervised semantic similarity based retrieval"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "an overview of multi task learning in deep neural networks"
    - "a deep learning approach for expert identification in question answering communities"

Connected through hyperedge HE-4 (induced by source paper "visual question answering datasets algorithms and future challenges"):
  with members:
    - "hadamard product for low rank bilinear pooling"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "dual attention networks for multimodal reasoning and matching"
    - "analyzing the behavior of visual question answering models"
    - "a focused dynamic attention model for visual question answering"
    - "multimodal residual learning for visual qa"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

Connected through hyperedge HE-5 (induced by source paper "what s in a question using visual questions as a form of supervision"):
  with members:
    - "training recurrent answering units with joint loss minimization for vqa"
    - "leveraging visual question answering for image caption ranking"
    - "a focused dynamic attention model for visual question answering"
    - "ambient sound provides supervision for visual learning"
    - "segmentation from natural language expressions"
    - "spice semantic propositional image caption evaluation"
    - "multimodal residual learning for visual qa"
    - "towards weakly supervised action localization"

Connected through hyperedge HE-6 (induced by source paper "don t just assume look and answer overcoming priors for visual question answering"):
  with members:
    - "an empirical evaluation of visual question answering for novel objects"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "being negative but constructively lessons learnt from creating better visual question answering datasets"
    - "an analysis of visual question answering algorithms"
    - "learning to generalize to new compositions in image understanding"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "analyzing the behavior of visual question answering models"
    - "a focused dynamic attention model for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "an analysis of visual question answering algorithms"):
  with members:
    - "visual question answering datasets algorithms and future challenges"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "a focused dynamic attention model for visual question answering"
    - "multimodal residual learning for visual qa"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning to compose neural networks for question answering"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "multi modal factorized bilinear pooling with co attention learning for visual question answering"):
  with members:
    - "hadamard product for low rank bilinear pooling"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "dual attention networks for multimodal reasoning and matching"
    - "a focused dynamic attention model for visual question answering"
    - "multimodal residual learning for visual qa"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning to compose neural networks for question answering"
    - "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)