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 "adaptive wasserstein hourglass for weakly supervised hand pose estimation from monocular rgb"):
  Abstract: Insufficient labeled training datasets is one of the bottlenecks of 3D hand pose estimation from monocular RGB images. Synthetic datasets have a large number of images with precise annotations, but the obvious difference with real-world datasets impacts the generalization. Little work has been done to bridge the gap between two domains over their wide difference. In this paper, we propose a domain adaptation method called Adaptive Wasserstein Hourglass (AW Hourglass) for weakly-supervised 3D hand pose estimation, which aims to distinguish the difference and explore the common characteristics (e.g. hand structure) of synthetic and real-world datasets. Learning the common characteristics helps the network focus on pose-related information. The similarity of the characteristics makes it easier to enforce domain-invariant constraints. During training, based on the relation between these common characteristics and 3D pose learned from fully-annotated synthetic datasets, it is beneficial for the network to restore the 3D pose of weakly labeled real-world datasets with the aid of 2D annotations and depth images. While in testing, the network predicts the 3D pose with the input of RGB.

Includes members:
  Member "3d hand shape and pose estimation from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning nonparametric human mesh reconstruction from a single image without ground truth meshes")
  Member "3d hand shape and pose from images in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "domain transfer for 3d pose estimation from color images without manual annotations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "honnotate a method for 3d annotation of hand and objects poses")
  Member "3d human pose estimation in the wild by adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "tadam task dependent adaptive metric for improved few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to compare relation semantic alignment for few shot learning")
  Member "hand pose estimation via latent 2 5d heatmap regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation")
  Member "cross modal deep variational hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "large scale multiview 3d hand pose dataset":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")

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)