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: "360 panorama synthesis from a sparse set of images with unknown fov"
  Abstract: 360 images represent scenes captured in all possible viewing directions. They enable viewers to navigate freely around the scene and thus provide an immersive experience. Conversely, conventional images represent scenes in a single viewing direction. These images are captured with a small or limited field of view. As a result, only some parts of the scenes are observed, and valuable information about the surroundings is lost. We propose a learning-based approach that reconstructs the scene in 360 x180 from conventional images. This approach first estimates the field of view of input images relative to the panorama. The estimated field of view is then used as the prior for synthesizing a high-resolution 360 panoramic output. Experimental results demonstrate that our approach outperforms alternative method and is robust enough to synthesize real-world data (e.g. scenes captured using smartphones).

Connected through hyperedge HE-1 (induced by source paper "spherical image generation from a single normal field of view image by considering scene symmetry"):
  with members:
    - "pluralistic image completion"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "spherephd applying cnns on a spherical polyhedron representation of 360 degree images"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "generative face completion"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "learning so 3 equivariant representations with spherical cnns"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

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)