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 "human synthesis and scene compositing"):
  Abstract: Generating good quality and geometrically plausible synthetic images of humans with the ability to control appearance, pose and shape parameters, has become increasingly important for a variety of tasks ranging from photo editing, fashion virtual try-on, to special effects and image compression. In this paper, we propose HUSC, a HUman Synthesis and Scene Compositing framework for the realistic synthesis of humans with different appearance, in novel poses and scenes. Central to our formulation is 3d reasoning for both people and scenes, in order to produce realistic collages, by correctly modeling perspective effects and occlusion, by taking into account scene semantics and by adequately handling relative scales. Conceptually our framework consists of three components: (1) a human image synthesis model with controllable pose and appearance, based on a parametric representation, (2) a person insertion procedure that leverages the geometry and semantics of the 3d scene, and (3) an appearance compositing process to create a seamless blending between the colors of the scene and the generated human image, and avoid visual artifacts. The performance of our framework is supported by both qualitative and quantitative results, in particular state-of-the art synthesis scores for the DeepFashion dataset.

Includes members:
  Member "dense intrinsic appearance flow for human pose transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "soft gated warping gan for pose guided person image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "learning to segment via cut and paste":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "synthesizing images of humans in unseen poses":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "unsupervised person image synthesis in arbitrary poses":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "instance level human parsing via part grouping network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "modeling visual context is key to augmenting object detection datasets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mixup regularization for region proposal based object detectors")
  Member "a variational u net for conditional appearance and shape generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi channel attention selection gans for guided image to image translation")

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)