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: "separating reflection and transmission images in the wild"
  Abstract: The reflections caused by common semi-reflectors, such as glass windows, can severely impact the performance of computer vision algorithms. State-of-the-art works can successfully remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and fail to generalize to real-world images---even when they leverage polarization. We present a deep learning approach to separate the reflected and the transmitted components of the recorded irradiance. Key to our approach is our synthetic data generation, which accurately simulates reflections, including those generated by curved and non-ideal surfaces, and non-static scenes. We extensively validate our method against a number of related works on a new dataset of images captured in the wild.

Connected through hyperedge HE-1 (induced by source paper "the mertens unrolled network mu net a high dynamic range fusion neural network for through the windshield driver recognition"):
  with members:
    - "high dynamic range image forensics using cnn"
    - "retinaface single stage dense face localisation in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "expandnet a deep convolutional neural network for high dynamic range expansion from low dynamic range content"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "recurrent inference machines for solving inverse problems"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-2 (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"
    - "reflection separation using guided annotation"

Connected through hyperedge HE-3 (induced by source paper "semantic guided single image reflection removal"):
  with members:
    - "single image reflection separation with perceptual losses"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "crrn multi scale guided concurrent reflection removal network"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "robust optical flow estimation of double layer images under transparency or reflection"

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)