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 3d pose estimation from a single image using multi view consistency"):
  Abstract: We present a novel data-driven regularizer for weakly-supervised learning of 3D#R##N#human pose estimation that eliminates the drift problem that affects existing approaches.#R##N#We do this by moving the stereo reconstruction problem into the loss of the network#R##N#itself. This avoids the need to reconstruct 3D data prior to training and unlike previous#R##N#semi-supervised approaches, avoids the need for a warm-up period of supervised training.#R##N#The conceptual and implementational simplicity of our approach is fundamental to its#R##N#appeal. Not only is it straightforward to augment many weakly-supervised approaches#R##N#with our additional re-projection based loss, but it is obvious how it shapes reconstructions#R##N#and prevents drift. As such we believe it will be a valuable tool for any researcher working#R##N#in weakly-supervised 3D reconstruction. Evaluating on Panoptic, the largest multi-camera#R##N#and markerless dataset available, we obtain an accuracy that is essentially indistinguishable#R##N#from a strongly-supervised approach making full use of 3D groundtruth in training.

Includes members:
  Member "lifting from the deep convolutional 3d pose estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")
  Member "compositional human pose regression":
    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 "a simple yet effective baseline for 3d human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "hand keypoint detection in single images using multiview bootstrapping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "coarse to fine volumetric prediction for single image 3d human pose":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "learning to fuse 2d and 3d image cues for monocular body pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hemlets posh learning part centric heatmap triplets for 3d human pose and shape estimation")
  Member "keep it smpl automatic estimation of 3d human pose and shape from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)