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 "an end to end framework for unconstrained monocular 3d hand pose estimation"):
  Abstract: This work addresses the challenging problem of unconstrained 3D hand pose estimation using monocular RGB images. Most of the existing approaches assume some prior knowledge of hand (such as hand locations and side information) is available for 3D hand pose estimation. This restricts their use in unconstrained environments. We, therefore, present an end-to-end framework that robustly predicts hand prior information and accurately infers 3D hand pose by learning ConvNet models while only using keypoint annotations. To achieve robustness, the proposed framework uses a novel keypoint-based method to simultaneously predict hand regions and side labels, unlike existing methods that suffer from background color confusion caused by using segmentation or detection-based technology. Moreover, inspired by the biological structure of the human hand, we introduce two geometric constraints directly into the 3D coordinates prediction that further improves its performance in a weakly-supervised training. Experimental results show that our proposed framework not only performs robustly on unconstrained setting, but also outperforms the state-of-art methods on standard benchmark datasets.

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 "learning joint reconstruction of hands and manipulated objects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "3d hand shape and pose from images in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "single image 3d hand reconstruction with mesh convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d hand pose estimation in the wild via graph refinement under adversarial learning")
  Member "analysis of hand segmentation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis of the hands in egocentric vision a survey")
  Member "cross modal deep variational hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "learning to estimate 3d hand pose from single rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "using a single rgb frame for real time 3d hand pose estimation in the wild":
    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)