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 correction via deep reciprocating hdr transformation"):
  Abstract: Image correction aims to adjust an input image into a visually pleasing one. Existing approaches are proposed mainly from the perspective of image pixel manipulation. They are not effective to recover the details in the under/over exposed regions. In this paper, we revisit the image formation procedure and notice that the missing details in these regions exist in the corresponding high dynamic range (HDR) data. These details are well perceived by the human eyes but diminished in the low dynamic range (LDR) domain because of the tone mapping process. Therefore, we formulate the image correction task as an HDR transformation process and propose a novel approach called Deep Reciprocating HDR Transformation (DRHT). Given an input LDR image, we first reconstruct the missing details in the HDR domain. We then perform tone mapping on the predicted HDR data to generate the output LDR image with the recovered details. To this end, we propose a united framework consisting of two CNNs for HDR reconstruction and tone mapping. They are integrated end-to-end for joint training and prediction. Experiments on the standard benchmarks demonstrate that the proposed method performs favorably against state-of-the-art image correction methods.

Includes members:
  Member "stylizing face images via multiple exemplars":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint face hallucination and deblurring via structure generation and detail enhancement")
  Member "msr net low light image enhancement using deep convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "hdr image reconstruction from a single exposure using deep cnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lighthouse predicting lighting volumes for spatially coherent illumination")
  Member "learning high dynamic range from outdoor panoramas":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deeplight learning illumination for unconstrained mobile mixed reality")
  Member "video deblurring via semantic segmentation and pixel wise non linear kernel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments")
  Member "learning deep cnn denoiser prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse weight activation training")
  Member "segflow joint learning for video object segmentation and optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "memnet a persistent memory network for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)