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: "dynamic fusion with intra and inter modality attention flow for visual question answering"
  Abstract: Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fusing multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities. It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering. We also show that the proposed dynamic intra-modality attention flow conditioned on the other modality can dynamically modulate the intra-modality attention of the target modality, which is vital for multimodality feature fusion. Experimental evaluations on the VQA 2.0 dataset show that the proposed method achieves state-of-the-art VQA performance. Extensive ablation studies are carried out for the comprehensive analysis of the proposed method.

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"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-2 (induced by source paper "multi modality latent interaction network for visual question answering"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "weakly supervised compositional featureaggregation for few shot recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "2nd place solution to the gqa challenge 2019"
    - "question type guided attention in visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"):
  with members:
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "videobert a joint model for video and language representation learning"
    - "cross lingual language model pretraining"
    - "nocaps novel object captioning at scale"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modulating early visual processing by language"

Connected through hyperedge HE-4 (induced by source paper "learning rich image region representation for visual question answering"):
  with members:
    - "bilinear attention networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "mask r cnn"
    - "aggregated residual transformations for deep neural networks"

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)