human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "unpaired image captioning via scene graph alignments"):
  Abstract: Most of the existing deep learning based image captioning methods are fully-supervised models, which require large-scale paired image-caption datasets. However, getting large scale image-caption paired data is labor-intensive and time-consuming. In this paper, we present a scene graph based approach for unpaired image captioning. Our framework comprises an image scene graph generator, a sentence scene graph generator, a scene graph encoder, and a sentence decoder. Specifically, we first train the scene graph encoder and the sentence decoder on the text modality. To align the scene graphs between images and sentences, we propose an unsupervised feature alignment method that maps the scene graph features from the image modality to the sentence modality without any paired data. Experimental results show that our proposed model can generate quite promising results without using any image-caption training pairs, outperforming existing methods by a wide margin.

Includes members:
  Member "learning to collocate neural modules for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deconfounded image captioning a causal retrospect")
  Member "scene graph generation with external knowledge and image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object centric image generation from layouts")
  Member "semantic correlation promoted shape variant context for segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation")
  Member "boundary aware feature propagation for scene segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks")
  Member "shuffle then assemble learning object agnostic visual relationship features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graphical contrastive losses for scene graph parsing")
  Member "unsupervised image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction")
  Member "unpaired image captioning by language pivoting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semantic correlation promoted shape variant context for segmentation")
  Member "engaging image captioning via personality":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "captioning images taken by people who are blind")

Question: Please tell me which class the source paper 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)