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: "learning a discriminative model for the perception of realism in composite images"
  Abstract: What makes an image appear realistic? In this work, we are answering this question from a data-driven perspective by learning the perception of visual realism directly from large amounts of data. In particular, we train a Convolutional Neural Network (CNN) model that distinguishes natural photographs from automatically generated composite images. The model learns to predict visual realism of a scene in terms of color, lighting and texture compatibility, without any human annotations pertaining to it. Our model outperforms previous works that rely on hand-crafted heuristics, for the task of classifying realistic vs. unrealistic photos. Furthermore, we apply our learned model to compute optimal parameters of a compositing method, to maximize the visual realism score predicted by our CNN model. We demonstrate its advantage against existing methods via a human perception study.

Connected through hyperedge HE-1 (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-2 (induced by source paper "cnn generated images are surprisingly easy to spot for now"):
  with members:
    - "seeing what a gan cannot generate"
    - "detecting gan generated fake images using co occurrence matrices"
    - "faceforensics learning to detect manipulated facial images"
    - "analyzing and improving the image quality of stylegan"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "detecting and simulating artifacts in gan fake images"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-3 (induced by source paper "improving the harmony of the composite image by spatial separated attention module"):
  with members:
    - "inserting videos into videos"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "temporally coherent video harmonization using adversarial networks"
    - "attention gan for object transfiguration in wild images"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-4 (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-5 (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"
    - "deep image harmonization"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "identity mappings in deep residual networks"
    - "direct sparse odometry"
    - "horizon lines in the wild"

Connected through hyperedge HE-6 (induced by source paper "realistic hair simulation using image blending"):
  with members:
    - "lucid data dreaming for video object segmentation"
    - "synthesizing training data for object detection in indoor scenes"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "a comparative study of algorithms for realtime panoramic video blending"

Connected through hyperedge HE-7 (induced by source paper "image harmonization datasets hcoco hadobe5k hflickr and hday2night"):
  with members:
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep image harmonization"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "automatic content aware color and tone stylization"

Connected through hyperedge HE-8 (induced by source paper "deep image harmonization via domain verification"):
  with members:
    - "improving the harmony of the composite image by spatial separated attention module"
    - "deep image harmonization"

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)