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: "graph reasoning networks for visual question answering"
  Abstract: The interaction between language and visual information has been emphasized in visual question answering (VQA) with the help of attention mechanism. However, the relationship between words in question has been underestimated, which makes it hard to answer questions that involve the relationship between multiple entities, such as comparison and counting. In this paper, we develop the graph reasoning networks to tackle this problem. Two kinds of graphs are investigated, namely inter-graph and intra-graph. The inter-graph transfers features of the detected objects to their related query words, enabling the output nodes to have both semantic and factual information. The intra-graph exchanges information between these output nodes from inter-graph to amplify implicit yet important relationship between objects. These two kinds of graphs cooperate with each other, and thus our resulting model can reason the relationship and dependence between objects, which leads to realization of multi-step reasoning. Experimental results on the GQA v1.1 dataset demonstrate the reasoning ability of our method to handle compositional questions about real-world images. We achieve state-of-the-art performance, boosting accuracy to 57.04%. On the VQA 2.0 dataset, we also receive a promising improvement on overall accuracy, especially on counting problem.

Connected through hyperedge HE-1 (induced by source paper "meta module network for compositional visual reasoning"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "learning by abstraction the neural state machine"
    - "language conditioned graph networks for relational reasoning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "probabilistic neural symbolic models for interpretable visual question answering"

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)