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: "image question answer synergistic network for visual dialog"
  Abstract: The image, question (combined with the history for de-referencing), and the corresponding answer are three vital components of visual dialog. Classical visual dialog systems integrate the image, question, and history to search for or generate the best matched answer, and so, this approach significantly ignores the role of the answer. In this paper, we devise a novel image-question-answer synergistic network to value the role of the answer for precise visual dialog. We extend the traditional one-stage solution to a two-stage solution. In the first stage, candidate answers are coarsely scored according to their relevance to the image and question pair. Afterward, in the second stage, answers with high probability of being correct are re-ranked by synergizing with image and question. On the Visual Dialog v1.0 dataset, the proposed synergistic network boosts the discriminative visual dialog model to achieve a new state-of-the-art of 57.88\% normalized discounted cumulative gain. A generative visual dialog model equipped with the proposed technique also shows promising improvements.

Connected through hyperedge HE-1 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog"):
  with members:
    - "understanding chat messages for sticker recommendation in hike messenger"
    - "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"
    - "product aware answer generation in e commerce question answering"
    - "how to write summaries with patterns learning towards abstractive summarization through prototype editing"
    - "abstractive text summarization by incorporating reader comments"
    - "think visually question answering through virtual imagery"
    - "multimodal emoji prediction"
    - "transfer learning via unsupervised task discovery for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "modality balanced models for visual dialogue"):
  with members:
    - "dual attention networks for visual reference resolution in 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-3 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in 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-4 (induced by source paper "large scale pretraining for visual dialog a simple state of the art baseline"):
  with members:
    - "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"
    - "dual attention networks for visual reference resolution in visual dialog"

Connected through hyperedge HE-5 (induced by source paper "heterogeneous graph learning for visual commonsense reasoning"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "information maximizing visual question generation"
    - "from recognition to cognition visual commonsense reasoning"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "learning to compose dynamic tree structures for visual contexts"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "making history matter history advantage sequence training for visual dialog"):
  with members:
    - "dual attention networks for visual reference resolution in 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-7 (induced by source paper "dualvd an adaptive dual encoding model for deep visual understanding in visual dialogue"):
  with members:
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "factor graph attention"
    - "relation aware graph attention network for visual question answering"
    - "visual question answering as reading comprehension"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"

Connected through hyperedge HE-8 (induced by source paper "dmrm a dual channel multi hop reasoning model for visual dialog"):
  with members:
    - "fusion of detected objects in text for visual question answering"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "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.CL(Computation and Language)