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: "improving image captioning by leveraging knowledge graphs"
  Abstract: We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning. The results of our experiments, on several benchmark data sets such as MS COCO, as measured by CIDEr-D, a performance metric for image captioning, show that the variants of the state-of-the-art methods for image captioning that make use of the information extracted from knowledge graphs can substantially outperform those that rely solely on the information extracted from images.

Connected through hyperedge HE-1 (induced by source paper "an ontology aware framework for audio event classification"):
  with members:
    - "multi label image recognition with graph convolutional networks"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolutional networks for text classification"
    - "geometric deep learning going beyond euclidean data"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "cnn architectures for large scale audio classification"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "learning to caption images with two stream attention and sentence auto encoder"):
  with members:
    - "attention on attention for image captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "auto encoding scene graphs for image captioning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "convolutional sequence to sequence learning"
    - "sequence to sequence learning as beam search optimization"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "relational reasoning using prior knowledge for visual captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "learning visual knowledge memory networks for visual question answering"
    - "object hallucination in image captioning"
    - "auto encoding scene graphs for image captioning"
    - "image generation from scene graphs"
    - "exploring visual relationship for image captioning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

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)