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: "a focused dynamic attention model for visual question answering"
  Abstract: Visual Question and Answering (VQA) problems are attracting increasing interest from multiple research disciplines. Solving VQA problems requires techniques from both computer vision for understanding the visual contents of a presented image or video, as well as the ones from natural language processing for understanding semantics of the question and generating the answers. Regarding visual content modeling, most of existing VQA methods adopt the strategy of extracting global features from the image or video, which inevitably fails in capturing fine-grained information such as spatial configuration of multiple objects. Extracting features from auto-generated regions -- as some region-based image recognition methods do -- cannot essentially address this problem and may introduce some overwhelming irrelevant features with the question. In this work, we propose a novel Focused Dynamic Attention (FDA) model to provide better aligned image content representation with proposed questions. Being aware of the key words in the question, FDA employs off-the-shelf object detector to identify important regions and fuse the information from the regions and global features via an LSTM unit. Such question-driven representations are then combined with question representation and fed into a reasoning unit for generating the answers. Extensive evaluation on a large-scale benchmark dataset, VQA, clearly demonstrate the superior performance of FDA over well-established baselines.

Connected through hyperedge HE-1 (induced by source paper "multimodal unified attention networks for vision and language interactions"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "multimodal deep network embedding with integrated structure and attribute information"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "dynamic fusion with intra and inter modality attention flow for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "transfer learning in visual and relational reasoning"):
  with members:
    - "leveraging medical visual question answering with supporting facts"
    - "learning by abstraction the neural state machine"
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "huggingface s transformers state of the art natural language processing"
    - "do better imagenet models transfer better"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "taskonomy disentangling task transfer learning"
    - "object based reasoning in vqa"

Connected through hyperedge HE-3 (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"
    - "dual attention networks for visual reference resolution in 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"

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"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-5 (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"
    - "dualnet domain invariant network 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-6 (induced by source paper "visual query answering by entity attribute graph matching and reasoning"):
  with members:
    - "exploring visual relationship for image captioning"
    - "learning to reason end to end module networks for visual question answering"
    - "detecting visual relationships with deep relational networks"
    - "structured attentions for visual question answering"
    - "scene graph generation from objects phrases and region captions"
    - "accurate parameter estimation for bayesian network classifiers using hierarchical dirichlet processes"
    - "graph structured representations for visual question answering"
    - "boosting image captioning with attributes"

Connected through hyperedge HE-7 (induced by source paper "chartnet visual reasoning over statistical charts using mac networks"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "hadamard product for low rank bilinear pooling"
    - "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 "learning visual question answering by bootstrapping hard attention"):
  with members:
    - "hyperbolic attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "self attention with relative position representations"
    - "object based reasoning in vqa"
    - "relational inductive biases deep learning and graph networks"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"

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)