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 attention branch network for perspective free sentence generation"
  Abstract: In this paper, we address the automatic sentence generation of fetching instructions for domestic service robots. Typical fetching commands such as "bring me the yellow toy from the upper part of the white shelf" includes referring expressions, i.e., "from the white upper part of the white shelf". To solve this task, we propose a multimodal attention branch network (Multi-ABN) which generates natural sentences in an end-to-end manner. Multi-ABN uses multiple images of the same fixed scene to generate sentences that are not tied to a particular viewpoint. This approach combines a linguistic attention branch mechanism with several attention branch mechanisms. We evaluated our approach, which outperforms the state-of-the-art method on a standard metrics. Our method also allows us to visualize the alignment between the linguistic input and the visual features.

Connected through hyperedge HE-1 (induced by source paper "a formal analysis of multimodal referring strategies under common ground"):
  with members:
    - "grounding language attributes to objects using bayesian eigenobjects"
    - "generating justifications for norm related agent decisions"
    - "multimodal continuation style architectures for human robot interaction"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "neuralreg an end to end approach to referring expression generation"
    - "multimodal grounding for language processing"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "the something something video database for learning and evaluating visual common sense"

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"
    - "multimodal unified attention networks for vision and language interactions"
    - "grounding language attributes to objects using bayesian eigenobjects"
    - "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)