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 "enhancing underexposed photos using perceptually bidirectional similarity"):
  Abstract: Although remarkable progress has been made, existing methods for enhancing underexposed photos tend to produce visually unpleasing results due to the existence of visual artifacts (e.g., color distortion, loss of details and uneven exposure). We observed that this is because they fail to ensure the perceptual consistency of visual information between the source underexposed image and its enhanced output. To obtain high-quality results free of these artifacts, we present a novel underexposed photo enhancement approach that is able to maintain the perceptual consistency. We achieve this by proposing an effective criterion, referred to as perceptually bidirectional similarity (PBS), which explicitly describes how to ensure the perceptual consistency when enhancing underexposed images. Particularly, we adopt the Retinex theory and cast the enhancement problem as PBS-constrained illumination estimation, where we formulate PBS as constraints on illumination and solve for the illumination which can recover the desired artifact-free enhancement results. In addition, we describe a video enhancement framework that adopts the presented illumination estimation for handling underexposed videos. To this end, a probabilistic approach is introduced to propagate illuminations of sampled keyframes to the entire video by tackling a Bayesian Maximum A Posteriori (MAP) problem. Extensive experiments demonstrate the superiority of our method over the state-of-the-art methods.

Includes members:
  Member "kindling the darkness a practical low light image enhancer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "enlightengan deep light enhancement without paired supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "zero reference deep curve estimation for low light image enhancement")
  Member "deep retinex decomposition for low light enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "image correction via deep reciprocating hdr transformation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "human aware motion deblurring")
  Member "learning to see in the dark":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "deep bilateral learning for real time image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "llnet a deep autoencoder approach to natural low light image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "automatic photo adjustment using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "replacing mobile camera isp with a single deep learning model")

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)