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: "revisiting deep intrinsic image decompositions"
  Abstract: While invaluable for many computer vision applications, decomposing a natural image into intrinsic reflectance and shading layers represents a challenging, underdetermined inverse problem. As opposed to strict reliance on conventional optimization or filtering solutions with strong prior assumptions, deep learning-based approaches have also been proposed to compute intrinsic image decompositions when granted access to sufficient labeled training data. The downside is that current data sources are quite limited, and broadly speaking fall into one of two categories: either dense fully-labeled images in synthetic/narrow settings, or weakly-labeled data from relatively diverse natural scenes. In contrast to many previous learning-based approaches, which are often tailored to the structure of a particular dataset (and may not work well on others), we adopt core network structures that universally reflect loose prior knowledge regarding the intrinsic image formation process and can be largely shared across datasets. We then apply flexibly supervised loss layers that are customized for each source of ground truth labels. The resulting deep architecture achieves state-of-the-art results on all of the major intrinsic image benchmarks, and runs considerably faster than most at test time.

Connected through hyperedge HE-1 (induced by source paper "stereoscopic neural style transfer"):
  with members:
    - "artistic style transfer for videos and spherical images"
    - "continuous 3d label stereo matching using local expansion moves"
    - "towards open set identity preserving face synthesis"
    - "learning selfie friendly abstraction from artistic style images"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "characterizing and improving stability in neural style transfer"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "stylebank an explicit representation for neural image style transfer"

Connected through hyperedge HE-2 (induced by source paper "intrinsic image transformation via scale space decomposition"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "neural face editing with intrinsic image disentangling"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "cvae gan fine grained image generation through asymmetric training"
    - "photographic image synthesis with cascaded refinement networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "darn a deep adversial residual network for intrinsic image decomposition"

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)