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 "star a structure and texture aware retinex model"):
  Abstract: Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives are attributed to the changes in piece-wise constant reflectance, while smaller derivatives are emerged in the smooth illumination. In this paper, we propose to utilize the exponentiated derivatives (with an exponent $\gamma$) of an observed image to generate a structure map when being amplified with $\gamma>1$ and a texture map when being shrank with $\gamma<1$. To this end, we design exponential filters for the local derivatives, and present their capability on extracting accurate structure and texture maps, influenced by the choices of exponents $\gamma$ on the local derivatives. The extracted structure and texture maps are employed to regularize the illumination and reflectance components in Retinex decomposition. A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image. We solve the STAR model in an alternating minimization manner. Each sub-problem is transformed into a vectorized least squares regression with closed-form solution. Comprehensive experiments demonstrate that, the proposed STAR model produce better quantitative and qualitative performance than previous competing methods, on illumination and reflectance estimation, low-light image enhancement, and color correction. The code will be publicly released.

Includes members:
  Member "noisy as clean learning unsupervised denoising from the corrupted image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "first image then video a two stage network for spatiotemporal video denoising")
  Member "nlh a blind pixel level non local method for real world image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semi supervised self growing generative adversarial networks for image recognition")
  Member "salient object detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross layer feature pyramid network for salient object detection")
  Member "a trilateral weighted sparse coding scheme for real world image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "simultaneous fidelity and regularization learning for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nlh a blind pixel level non local method for real world image denoising")
  Member "scaled simplex representation for subspace clustering":
    also connected through other hyperedges:
      (none)
  Member "color constancy by gans an experimental survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bag of color features for color constancy")
  Member "external prior guided internal prior learning for real world noisy image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization")

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)