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: "hole filling with multiple reference views in dibr view synthesis"
  Abstract: Depth-image-based rendering (DIBR) oriented view synthesis has been widely employed in the current depth-based 3-D video systems by synthesizing a virtual view from an arbitrary viewpoint. However, holes may appear in the synthesized view due to disocclusion, thus significantly degrading the quality. Consequently, efforts have been made on developing effective and efficient hole-filling algorithms. Current hole-filling techniques generally extrapolate/interpolate the hole regions with the neighboring information based on an assumption that the texture pattern in the holes is similar to that of the neighboring background information. However, in many scenarios, especially of complex texture, the assumption may not hold. In other words, hole-filling techniques can only provide an estimation for a hole which may not be good enough or may even be erroneous considering a wide variety of complex scene of images. In this paper, we first examine the view interpolation with multiple reference views, demonstrating that the problem of emerging holes in a target virtual view can be greatly alleviated by making good use of other neighboring complementary views in addition to its two (commonly used) most neighboring primary views. The effects of using multiple views for view extrapolation in reducing holes are also investigated in this paper. In view of the 3D Video and ongoing free-viewpoint TV standardization, we propose a new view synthesis framework, which employs multiple views to synthesize output virtual views. Furthermore, a scheme of selective warping of complementary views is developed by efficiently locating a small number of useful pixels in the complementary views for hole reduction, to avoid full warping of additional complementary views thus lowering greatly the warping complexity. Experimental results show that the hole size based on two primary reference views may be reduced by up to about 70% with the help of two complementary reference views in the case of view interpolation, while the hole size based on one primary reference view may be reduced by about 27% with the help of one more complementary reference view in view extrapolation. Moreover, it is shown that by using one more pair of views in view interpolation and one more view in view extrapolation, 10% hole pixels may be reduced additionally.

Connected through hyperedge HE-1 (induced by source paper "grounded and controllable image completion by incorporating lexical semantics"):
  with members:
    - "universal dependency parsing from scratch"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "unconstrained foreground object search"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "mc gan multi conditional generative adversarial network for image synthesis"

Connected through hyperedge HE-2 (induced by source paper "depth map estimation for free viewpoint television"):
  with members:
    - "occlusion model guided antiocclusion depth estimation in light field"
    - "single view and multi view depth fusion"

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)