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: "dual attention networks for visual reference resolution in visual dialog"
  Abstract: Visual dialog (VisDial) is a task which requires an AI agent to answer a series of questions grounded in an image. Unlike in visual question answering (VQA), the series of questions should be able to capture a temporal context from a dialog history and exploit visually-grounded information. A problem called visual reference resolution involves these challenges, requiring the agent to resolve ambiguous references in a given question and find the references in a given image. In this paper, we propose Dual Attention Networks (DAN) for visual reference resolution. DAN consists of two kinds of attention networks, REFER and FIND. Specifically, REFER module learns latent relationships between a given question and a dialog history by employing a self-attention mechanism. FIND module takes image features and reference-aware representations (i.e., the output of REFER module) as input, and performs visual grounding via bottom-up attention mechanism. We qualitatively and quantitatively evaluate our model on VisDial v1.0 and v0.9 datasets, showing that DAN outperforms the previous state-of-the-art model by a significant margin (2.0% on NDCG).

Connected through hyperedge HE-1 (induced by source paper "vision dialog navigation by exploring cross modal memory"):
  with members:
    - "reasoning visual dialogs with structural and partial observations"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "factor graph attention"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "visual coreference resolution in visual dialog using neural module networks"

Connected through hyperedge HE-2 (induced by source paper "multi step joint modality attention network for scene aware dialogue system"):
  with members:
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "bilinear attention networks"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "tvqa localized compositional video question answering"
    - "recursive visual attention in visual dialog"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video question answering via attribute augmented attention network learning"
    - "visual reference resolution using attention memory for visual dialog"

Connected through hyperedge HE-3 (induced by source paper "modality balanced models for visual dialogue"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "recursive visual attention in visual dialog"
    - "mutan multimodal tucker fusion for visual question answering"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "dual attention networks for multimodal reasoning and matching"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"
    - "factor graph attention"
    - "relation aware graph attention network for visual question answering"
    - "flipdial a generative model for two way visual dialogue"
    - "ask no more deciding when to guess in referential visual dialogue"

Connected through hyperedge HE-5 (induced by source paper "large scale pretraining for visual dialog a simple state of the art baseline"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "vision and dialog navigation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"

Connected through hyperedge HE-6 (induced by source paper "efficient attention mechanism for handling all the interactions between many inputs with application to visual dialog"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "albert a lite bert for self supervised learning of language representations"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "deep modular co attention networks for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "making history matter history advantage sequence training for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "recursive visual attention in visual dialog"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "attention is all you need"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "visual reference resolution using attention memory for visual dialog"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"

Connected through hyperedge HE-8 (induced by source paper "dmrm a dual channel multi hop reasoning model for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "fusion of detected objects in text for visual question answering"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "murel multimodal relational reasoning for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "recursive visual attention in visual dialog"

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)