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: "where and who automatic semantic aware person composition"
  Abstract: Image compositing is a popular and successful method used to generate realistic yet fake imagery. Much previous work in compositing has focused on improving the appearance compatibility between a given object segment and a background image. However, most previous work does not investigate the topic of automatically selecting semantically compatible segments and predicting their locations and sizes given a background image. In this work, we attempt to fill this gap by developing a fully automatic compositing system that learns this information. To simplify the task, we restrict our problem by focusing on human instance composition, because human segments exhibit strong correlations with the background scene and are easy to collect. The first problem we investigate is determining where should a person segment be placed given a background image, and what should be its size in the background image. We tackle this by developing a novel Convolutional Neural Network (CNN) model that jointly predicts the potential location and size of the person segment. The second problem we investigate is, given the background image, which person segments (who) can be composited with the previously predicted locations and sizes, while retaining compatibility with both the local context and the global scene semantics? To achieve this, we propose an efficient context-based segment retrieval method that incorporates pre-trained deep feature representations. #R##N#To demonstrate the effectiveness of the proposed compositing system, we conduct quantitative and qualitative experiments including a user study. Experimental results show our system can generate composite images that look semantically and visually convincing. We also develop a proof-of-concept user interface to demonstrate the potential application of our method.

Connected through hyperedge HE-1 (induced by source paper "learning to generate synthetic data via compositing"):
  with members:
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "spatially transformed adversarial examples"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "loss rank mining a general hard example mining method for real time detectors"
    - "modeling visual context is key to augmenting object detection datasets"
    - "unsupervised hard example mining from videos for improved object detection"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "can 3d pose be learned from 2d projections alone"

Connected through hyperedge HE-2 (induced by source paper "generating 3d people in scenes without people"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning elementary structures for 3d shape generation and matching"
    - "does computer vision matter for action"
    - "resolving 3d human pose ambiguities with 3d scene constraints"
    - "the replica dataset a digital replica of indoor spaces"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "expressive body capture 3d hands face and body from a single image"
    - "imapper interaction guided scene mapping from monocular videos"

Connected through hyperedge HE-3 (induced by source paper "compact scene graphs for layout composition and patch retrieval"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "using scene graph context to improve image generation"
    - "bottom up object detection by grouping extreme and center points"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "relational inductive biases deep learning and graph networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi parametric image synthesis"
    - "image generation from scene graphs"

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"
    - "seeing what is not there learning context to determine where objects are missing"
    - "deep image harmonization"

Connected through hyperedge HE-5 (induced by source paper "what and where a context based recommendation system for object insertion"):
  with members:
    - "generative image inpainting with contextual attention"
    - "scene graph generation by iterative message passing"
    - "recent advance in content based image retrieval a literature survey"
    - "ssd single shot multibox detector"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "sift meets cnn a decade survey of instance retrieval"
    - "weakly supervised deep detection networks"
    - "parsenet looking wider to see better"

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.GR(Graphics)