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 "deep image blending"):
  Abstract: Image composition is an important operation to create visual content. Among image composition tasks, image blending aims to seamlessly blend an object from a source image onto a target image with lightly mask adjustment. A popular approach is Poisson image blending, which enforces the gradient domain smoothness in the composite image. However, this approach only considers the boundary pixels of target image, and thus can not adapt to texture of target image. In addition, the colors of the target image often seep through the original source object too much causing a significant loss of content of the source object. We propose a Poisson blending loss that achieves the same purpose of Poisson image blending. In addition, we jointly optimize the proposed Poisson blending loss as well as the style and content loss computed from a deep network, and reconstruct the blending region by iteratively updating the pixels using the L-BFGS solver. In the blending image, we not only smooth out gradient domain of the blending boundary but also add consistent texture into the blending region. User studies show that our method outperforms strong baselines as well as state-of-the-art approaches when placing objects onto both paintings and real-world images.

Includes members:
  Member "esrgan enhanced super resolution generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deep painterly harmonization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "singan learning a generative model from a single natural image")
  Member "generative image inpainting with contextual attention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "stable and controllable neural texture synthesis and style transfer using histogram losses":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "deep photo style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parameter free style projection for arbitrary style transfer")
  Member "gp gan towards realistic high resolution image blending":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "universal style transfer via feature transforms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "deep image harmonization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generating object stamps")

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)