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: "deep image harmonization"
  Abstract: Compositing is one of the most common operations in photo editing. To generate realistic composites, the appearances of foreground and background need to be adjusted to make them compatible. Previous approaches to harmonize composites have focused on learning statistical relationships between hand-crafted appearance features of the foreground and background, which is unreliable especially when the contents in the two layers are vastly different. In this work, we propose an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization. We also introduce an efficient way to collect large-scale and high-quality training data that can facilitate the training process. Experiments on the synthesized dataset and real composite images show that the proposed network outperforms previous state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "generating object stamps"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "adaptive composition gan towards realistic image synthesis"
    - "semantic image synthesis with spatially adaptive normalization"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "multimodal unsupervised image to image translation"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "adaptive composition gan towards realistic image synthesis"):
  with members:
    - "scene text synthesis for efficient and effective deep network training"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "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"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "visualizing the invisible occluded vehicle segmentation and recovery"):
  with members:
    - "path aggregation network for instance segmentation"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "learning to see the invisible end to end trainable amodal instance segmentation"
    - "generative image inpainting with contextual attention"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "segan segmenting and generating the invisible"

Connected through hyperedge HE-4 (induced by source paper "human synthesis and scene compositing"):
  with members:
    - "dense intrinsic appearance flow for human pose transfer"
    - "soft gated warping gan for pose guided person image synthesis"
    - "learning to segment via cut and paste"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "instance level human parsing via part grouping network"
    - "modeling visual context is key to augmenting object detection datasets"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-5 (induced by source paper "freihand a dataset for markerless capture of hand pose and shape from single rgb images"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "learning joint reconstruction of hands and manipulated objects"
    - "3d hand shape and pose from images in the wild"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "large scale multiview 3d hand pose dataset"
    - "learning to estimate 3d hand pose from single rgb images"

Connected through hyperedge HE-6 (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"
    - "guided image inpainting replacing an image region by pulling content from another image"
    - "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"

Connected through hyperedge HE-7 (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"
    - "guided image inpainting replacing an image region by pulling content from another image"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "clear skies ahead towards real time automatic sky replacement in video"):
  with members:
    - "icnet for real time semantic segmentation on high resolution images"
    - "segmenting sky pixels in images"
    - "sfm net learning of structure and motion from video"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "identity mappings in deep residual networks"
    - "direct sparse odometry"
    - "horizon lines in the wild"
    - "learning to refine object segments"

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)