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: "multimodal unified attention networks for vision and language interactions"
  Abstract: Learning an effective attention mechanism for multimodal data is important in many vision-and-language tasks that require a synergic understanding of both the visual and textual contents. Existing state-of-the-art approaches use co-attention models to associate each visual object (e.g., image region) with each textual object (e.g., query word). Despite the success of these co-attention models, they only model inter-modal interactions while neglecting intra-modal interactions. Here we propose a general `unified attention' model that simultaneously captures the intra- and inter-modal interactions of multimodal features and outputs their corresponding attended representations. By stacking such unified attention blocks in depth, we obtain the deep Multimodal Unified Attention Network (MUAN), which can seamlessly be applied to the visual question answering (VQA) and visual grounding tasks. We evaluate our MUAN models on two VQA datasets and three visual grounding datasets, and the results show that MUAN achieves top-level performance on both tasks without bells and whistles.

Connected through hyperedge HE-1 (induced by source paper "lxmert learning cross modality encoder representations from transformers"):
  with members:
    - "multi modality latent interaction network for visual question answering"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "exbert a visual analysis tool to explore learned representations in transformers models"
    - "videobert a joint model for video and language representation learning"
    - "deep modular co attention networks for visual question answering"
    - "visualbert a simple and performant baseline for vision and language"
    - "cross lingual language model pretraining"
    - "cycle consistency for robust visual question answering"

Connected through hyperedge HE-2 (induced by source paper "a multimodal target source classifier with attention branches to understand ambiguous instructions for fetching daily objects"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "grounding language attributes to objects using bayesian eigenobjects"
    - "multimodal attention branch network for perspective free sentence generation"
    - "tvqa spatio temporal grounding for video question answering"
    - "understanding natural language instructions for fetching daily objects using gan based multimodal target source classification"
    - "deep modular co attention networks for visual question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "interactive visual grounding of referring expressions for human robot interaction"

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)