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: "image inpainting using multi scale feature image translation"
  Abstract: We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into initialization and texture-refinement as two separate steps and model each step with a deep neural network. We also use simple heuristics to guide transferring of textures from boundary to the hole. We show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions of much smaller dimensionality. We evaluate our method on several public datasets and show that we not only generate results of comparable or better visual quality, but are orders of magnitude faster than previous state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "perceptually motivated method for image inpainting comparison"):
  with members:
    - "learning rich features for image manipulation detection"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "image splicing localization using a multi task fully convolutional network mfcn"
    - "shift net image inpainting via deep feature rearrangement"
    - "generative image inpainting with contextual attention"
    - "progressive neural architecture search"
    - "deep image prior"

Connected through hyperedge HE-2 (induced by source paper "unsupervised adversarial image inpainting"):
  with members:
    - "misgan learning from incomplete data with generative adversarial networks"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generative image inpainting with contextual attention"
    - "deep image prior"

Connected through hyperedge HE-3 (induced by source paper "probabilistic semantic inpainting with pixel constrained cnns"):
  with members:
    - "attentive neural processes"
    - "image inpainting for irregular holes using partial convolutions"
    - "neural processes"
    - "free form image inpainting with gated convolution"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "generative image inpainting with contextual attention"
    - "fast generation for convolutional autoregressive models"

Connected through hyperedge HE-4 (induced by source paper "empty cities image inpainting for a dynamic object invariant space"):
  with members:
    - "single view place recognition under seasonal changes"
    - "a closed form solution to photorealistic image stylization"
    - "dynamic objects segmentation for visual localization in urban environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "generative image inpainting with contextual attention"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "deep image prior"
    - "carla an open urban driving simulator"

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)