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: "generative single image reflection separation"
  Abstract: Single image reflection separation is an ill-posed problem since two scenes, a transmitted scene and a reflected scene, need to be inferred from a single observation. To make the problem tractable, in this work we assume that categories of two scenes are known. It allows us to address the problem by generating both scenes that belong to the categories while their contents are constrained to match with the observed image. A novel network architecture is proposed to render realistic images of both scenes based on adversarial learning. The network can be trained in a weakly supervised manner, i.e., it learns to separate an observed image without corresponding ground truth images of transmission and reflection scenes which are difficult to collect in practice. Experimental results on real and synthetic datasets demonstrate that the proposed algorithm performs favorably against existing methods.

Connected through hyperedge HE-1 (induced by source paper "single image reflection removal exploiting misaligned training data and network enhancements"):
  with members:
    - "group normalization"
    - "the relativistic discriminator a key element missing from standard gan"
    - "single image reflection removal using deep encoder decoder network"
    - "single image reflection separation with perceptual losses"
    - "the contextual loss for image transformation with non aligned data"
    - "crrn multi scale guided concurrent reflection removal network"
    - "context encoding for semantic segmentation"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-2 (induced by source paper "single image reflection removal through cascaded refinement"):
  with members:
    - "single image reflection removal exploiting misaligned training data and network enhancements"
    - "single image reflection removal using deep encoder decoder network"
    - "single image reflection separation with perceptual losses"
    - "crrn multi scale guided concurrent reflection removal network"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "reflection separation using guided annotation"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "hidden community detection in social networks"

Connected through hyperedge HE-3 (induced by source paper "blind visual motif removal from a single image"):
  with members:
    - "single image reflection separation with perceptual losses"
    - "generative image inpainting with contextual attention"
    - "image restoration by iterative denoising and backward projections"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "deep blind image inpainting"
    - "image de raining using a conditional generative adversarial network"
    - "xunit learning a spatial activation function for efficient image restoration"
    - "a generic deep architecture for single image reflection removal and image smoothing"

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)