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 "keypoint based weakly supervised human parsing"):
  Abstract: Fully convolutional networks (FCN) have achieved great success in human parsing in recent years. In conventional human parsing tasks, pixel-level labeling is required for guiding the training, which usually involves enormous human labeling efforts. To ease the labeling efforts, we propose a novel weakly supervised human parsing method which only requires simple object keypoint annotations for learning. We develop an iterative learning method to generate pseudo part segmentation masks from keypoint labels. With these pseudo masks, we train an FCN network to output pixel-level human parsing predictions. Furthermore, we develop a correlation network to perform joint prediction of part and object segmentation masks and improve the segmentation performance. The experiment results show that our weakly supervised method is able to achieve very competitive human parsing results. Despite our method only uses simple keypoint annotations for learning, we are able to achieve comparable performance with fully supervised methods which use the expensive pixel-level annotations.

Includes members:
  Member "weakly and semi supervised human body part parsing via pose guided knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "m2e try on net fashion from model to everyone":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "end to end learning of geometric deformations of feature maps for virtual try on")
  Member "multiple human parsing in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "measuring the utilization of public open spaces by deep learning a benchmark study at the detroit riverfront")
  Member "learning random walk label propagation for weakly supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised salient object detection via scribble annotations")
  Member "joint multi person pose estimation and semantic part segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "rmpe regional multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "refinenet multi path refinement networks for high resolution semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention guided chained context aggregation for semantic segmentation")

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)