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: "reflection separation using guided annotation"
  Abstract: Photographs taken through a glass surface often contain an approximately linear superposition of reflected and transmitted layers. Decomposing an image into these layers is generally an ill-posed task and the use of an additional image prior and user provided cues is presently necessary in order to obtain good results. Current annotation approaches rely on a strong sparsity assumption. For images with significant texture this assumption does not typically hold, thus rendering the annotation process unviable. In this paper we show that using a Gaussian Mixture Model patch prior, the correct local decomposition can almost always be found as one of 100 likely modes of the posterior. Thus, the user need only choose one of these modes in a sparse set of patches and the decomposition may then be completed automatically. We demonstrate the performance of our method using synthesized and real reflection images.

Connected through hyperedge HE-1 (induced by source paper "deep reflection prior"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "fast single image reflection suppression via convex optimization"
    - "single image reflection removal exploiting misaligned training data and network enhancements"
    - "single image reflection separation with perceptual losses"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "crrn multi scale guided concurrent reflection removal network"
    - "a generic deep architecture for single image reflection removal and image smoothing"

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"
    - "generative single image reflection separation"
    - "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"
    - "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 "single image reflection separation with perceptual losses"):
  with members:
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "fast image processing with fully convolutional networks"
    - "photographic image synthesis with cascaded refinement networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "multi scale context aggregation by dilated convolutions"
    - "loss functions for neural networks for image processing"

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)