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: "single image reflection removal using deep encoder decoder network"
  Abstract: Image of a scene captured through a piece of transparent and reflective material, such as glass, is often spoiled by a superimposed layer of reflection image. While separating the reflection from a familiar object in an image is mentally not difficult for humans, it is a challenging, ill-posed problem in computer vision. In this paper, we propose a novel deep convolutional encoder-decoder method to remove the objectionable reflection by learning a map between image pairs with and without reflection. For training the neural network, we model the physical formation of reflections in images and synthesize a large number of photo-realistic reflection-tainted images from reflection-free images collected online. Extensive experimental results show that, although the neural network learns only from synthetic data, the proposed method is effective on real-world images, and it significantly outperforms the other tested state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "controllable attention for structured layered video decomposition"):
  with members:
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "a short note about kinetics 600"
    - "single image reflection separation with perceptual losses"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-2 (induced by source paper "single image reflection removal exploiting misaligned training data and network enhancements"):
  with members:
    - "generative single image reflection separation"
    - "group normalization"
    - "the relativistic discriminator a key element missing from standard gan"
    - "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-3 (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 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-4 (induced by source paper "the visual centrifuge model free layered video representations"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "data augmentation by pairing samples for images classification"
    - "learning to extract a video sequence from a single motion blurred image"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "a short note about kinetics 600"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "single image reflection separation with perceptual losses"
    - "learning to separate object sounds by watching unlabeled video"

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)