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 "occlusion aware hand pose estimation using hierarchical mixture density network"):
  Abstract: Hand pose estimation is to predict the pose parameters representing a 3D hand model, such as locations of hand joints. This problem is very challenging due to large changes in viewpoints and articulations, and intense self-occlusions, etc. Many researchers have investigated the problem from both aspects of input feature learning and output prediction modelling. Though effective, most of the existing discriminative methods only give a deterministic estimation of target poses. Also, due to their single-value mapping intrinsic, they fail to adequately handle self-occlusion problems, where occluded joints present multiple modes. In this paper, we tackle the self-occlusion issue and provide a complete description of observed poses given an input depth image through a hierarchical mixture density network (HMDN) framework. In particular, HMDN leverages the state-of-the-art CNN module to facilitate feature learning, while proposes a density in a two-level hierarchy to reconcile single-valued and multi-valued mapping in the output. The whole framework is naturally end-to-end trainable with a mixture of two differentiable density functions. HMDN produces interpretable and diverse candidate samples, and significantly outperforms the state-of-the-art algorithms on benchmarks that exhibit occlusions.

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 "region ensemble network improving convolutional network for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast and accurate 3d hand pose estimation via recurrent neural network for capturing hand articulations")
  Member "bighand2 2m benchmark hand pose dataset and state of the art analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "handaugment a simple data augmentation method for depth based 3d hand pose estimation")
  Member "deepprior improving fast and accurate 3d hand pose estimation":
    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 "real time hand tracking under occlusion from an egocentric rgb d sensor":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "first person hand action benchmark with rgb d videos and 3d hand pose annotations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "end to end hand mesh recovery from a monocular rgb image")
  Member "training a feedback loop for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering")

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)