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 "weakly supervised body part parsing with pose based part priors"):
  Abstract: Human body part parsing refers to the task of predicting the semantic segmentation mask for each body part. Fully supervised body part parsing methods achieve good performances, but require an enormous amount of effort to annotate part masks for training. In contrast to high annotation costs required for a limited number of part mask annotations, a large number of weak labels such as poses and full body masks already exist and contain relevant information. Motivated by the possibility of using existing weak labels, we propose the first weakly supervised body part parsing framework. The basic idea is to train a parsing network with pose generated part priors that has blank uncertain regions on estimated boundaries, and use an iterative refinement module to generate new supervision and predictions on these regions. When sufficient extra weak supervisions are available, our weakly-supervised results (62.0% mIoU) on Pascal-Person-Part are comparable to the fully supervised state-of-the-art results (63.6% mIoU). Furthermore, in the extended semi-supervised setting, the proposed framework outperforms the state-of-art methods. In addition, we show that the proposed framework can be extended to other keypoint-supervised part parsing tasks such as face parsing.

Includes members:
  Member "graphonomy universal human parsing via graph transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "weakly and semi supervised human body part parsing via pose guided knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "synthesizing images of humans in unseen poses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "on regularized losses for weakly supervised cnn segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised salient object detection via scribble annotations")
  Member "human semantic parsing for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "weakly and semi supervised panoptic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards bounding box free panoptic segmentation")
  Member "look into person self supervised structure sensitive learning and a new benchmark for human parsing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical human parsing with typed part relation reasoning")

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)