human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "image restoration for under display camera"):
  Abstract: The new trend of full-screen devices encourages us to position a camera behind a screen. Removing the bezel and centralizing the camera under the screen brings larger display-to-body ratio and enhances eye contact in video chat, but also causes image degradation. In this paper, we focus on a newly-defined Under-Display Camera (UDC), as a novel real-world single image restoration problem. First, we take a 4k Transparent OLED (T-OLED) and a phone Pentile OLED (P-OLED) and analyze their optical systems to understand the degradation. Second, we design a novel Monitor-Camera Imaging System (MCIS) for easier real pair data acquisition, and a model-based data synthesizing pipeline to generate UDC data only from display pattern and camera measurements. Finally, we resolve the complicated degradation using learning-based methods. Our model demonstrates a real-time high-quality restoration trained with either real or synthetic data. The presented results and methods provide good practice to apply image restoration to real-world applications.

Includes members:
  Member "when awgn based denoiser meets real noises":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning raw image denoising with bayer pattern unification and bayer preserving augmentation")
  Member "dense haze a benchmark for image dehazing with dense haze and haze free images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "feature forwarding for efficient single image dehazing")
  Member "noise flow noise modeling with conditional normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization")
  Member "learning raw image denoising with bayer pattern unification and bayer preserving augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "content aware unsupervised deep homography estimation")
  Member "zoom to learn learn to zoom":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "camera lens super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "ffdnet toward a fast and flexible solution for cnn based image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "learning to synthesize motion blur":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "softmax splatting for video frame interpolation")

Question: Please tell me which class the source paper 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)