human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "guided image inpainting replacing an image region by pulling content from another image"
  Abstract: Deep generative models have shown success in automatically synthesizing missing image regions using surrounding context. However, users cannot directly decide what content to synthesize with such approaches. We propose an end-to-end network for image inpainting that uses a different image to guide the synthesis of new content to fill the hole. A key challenge addressed by our approach is synthesizing new content in regions where the guidance image and the context of the original image are inconsistent. We conduct four studies that demonstrate our results yield more realistic image inpainting results over seven baselines.

Connected through hyperedge HE-1 (induced by source paper "image inpainting a review"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "a novel approach for robust multi human action detection and recognition based on 3 dimentional convolutional neural networks"
    - "blind visual motif removal from a single image"
    - "adversarial inpainting of medical image modalities"
    - "deep hyperspectral prior denoising inpainting super resolution"
    - "pepsi fast and lightweight network for image inpainting"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "foreground aware image inpainting"

Connected through hyperedge HE-2 (induced by source paper "sc fegan face editing generative adversarial network with user s sketch and color"):
  with members:
    - "faceshop deep sketch based face image editing"
    - "image inpainting for irregular holes using partial convolutions"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "generative image inpainting with contextual attention"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"

Connected through hyperedge HE-3 (induced by source paper "coherent semantic attention for image inpainting"):
  with members:
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "the relativistic discriminator a key element missing from standard gan"
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"
    - "free form image inpainting with gated convolution"
    - "shift net image inpainting via deep feature rearrangement"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "contextual based image inpainting infer match and translate"

Connected through hyperedge HE-4 (induced by source paper "smart deep copy paste"):
  with members:
    - "faceshop deep sketch based face image editing"
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"
    - "deep bilateral learning for real time image enhancement"
    - "real time user guided image colorization with learned deep priors"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "unconstrained foreground object search"):
  with members:
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "compositional gan learning conditional image composition"
    - "compositional gan learning image conditional binary composition"
    - "generative image inpainting with contextual attention"
    - "billion scale similarity search with gpus"
    - "where and who automatic semantic aware person composition"
    - "seeing what is not there learning context to determine where objects are missing"
    - "deep image harmonization"

Connected through hyperedge HE-6 (induced by source paper "spatial fusion gan for image synthesis"):
  with members:
    - "scene text synthesis for efficient and effective deep network training"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "compositional gan learning conditional image composition"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "esir end to end scene text recognition via iterative image rectification"
    - "compositional gan learning image conditional binary composition"
    - "fast end to end trainable guided filter"

Connected through hyperedge HE-7 (induced by source paper "free form image inpainting with gated convolution"):
  with members:
    - "deep video inpainting"
    - "pepsi fast and lightweight network for image inpainting"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "spectral normalization for generative adversarial networks"

Question: Please tell me which class the center node 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)