human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "context aware synthesis and placement of object instances"):
  Abstract: Learning to insert an object instance into an image in a semantically coherent manner is a challenging and interesting problem. Solving it requires (a) determining a location to place an object in the scene and (b) determining its appearance at the location. Such an object insertion model can potentially facilitate numerous image editing and scene parsing applications. In this paper, we propose an end-to-end trainable neural network for the task of inserting an object instance mask of a specified class into the semantic label map of an image. Our network consists of two generative modules where one determines where the inserted object mask should be (i.e., location and scale) and the other determines what the object mask shape (and pose) should look like. The two modules are connected together via a spatial transformation network and jointly trained. We devise a learning procedure that leverage both supervised and unsupervised data and show our model can insert an object at diverse locations with various appearances. We conduct extensive experimental validations with comparisons to strong baselines to verify the effectiveness of the proposed network.

Includes members:
  Member "video to video synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "inferring semantic layout for hierarchical text to image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "binge watching scaling affordance learning from sitcoms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "st gan spatial transformer generative adversarial networks for image compositing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "pedestrian synthesis gan generating pedestrian data in real scene and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pmc gans generating multi scale high quality pedestrian with multimodal cascaded gans")
  Member "multimodal unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "semi parametric image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object centric image generation from layouts")

Question: Please tell me which class the source paper 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)