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 "hemlets pose learning part centric heatmap triplets for accurate 3d human pose estimation"):
  Abstract: Estimating 3D human pose from a single image is a challenging task. This work attempts to address the uncertainty of lifting the detected 2D joints to the 3D space by introducing an intermediate state - Part-Centric Heatmap Triplets (HEMlets), which shortens the gap between the 2D observation and the 3D interpretation. The HEMlets utilize three joint-heatmaps to represent the relative depth information of the end-joints for each skeletal body part. In our approach, a Convolutional Network (ConvNet) is first trained to predict HEMlests from the input image, followed by a volumetric joint-heatmap regression. We leverage on the integral operation to extract the joint locations from the volumetric heatmaps, guaranteeing end-to-end learning. Despite the simplicity of the network design, the quantitative comparisons show a significant performance improvement over the best-of-grade method (by 20% on Human3.6M). The proposed method naturally supports training with "in-the-wild" images, where only weakly-annotated relative depth information of skeletal joints is available. This further improves the generalization ability of our model, as validated by qualitative comparisons on outdoor images.

Includes members:
  Member "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "ordinal depth supervision for 3d human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "multi scale structure aware network for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation")
  Member "how robust is 3d human pose estimation to occlusion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation")
  Member "3d human pose estimation in the wild by adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "fbi pose towards bridging the gap between 2d images and 3d human poses using forward or backward information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hemlets posh learning part centric heatmap triplets for 3d human pose and shape estimation")
  Member "it s all relative monocular 3d human pose estimation from weakly supervised data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "learning pose grammar to encode human body configuration for 3d pose estimation":
    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)