human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "simultaneous hand pose and skeleton bone lengths estimation from a single depth image"
  Abstract: Articulated hand pose estimation is a challenging task for human-computer interaction. The state-of-the-art hand pose estimation algorithms work only with one or a few subjects for which they have been calibrated or trained. Particularly, the hybrid methods based on learning followed by model fitting or model based deep learning do not explicitly consider varying hand shapes and sizes. In this work, we introduce a novel hybrid algorithm for estimating the 3D hand pose as well as bone-lengths of the hand skeleton at the same time, from a single depth image. The proposed CNN architecture learns hand pose parameters and scale parameters associated with the bone-lengths simultaneously. Subsequently, a new hybrid forward kinematics layer employs both parameters to estimate 3D joint positions of the hand. For end-to-end training, we combine three public datasets NYU, ICVL and MSRA-2015 in one unified format to achieve large variation in hand shapes and sizes. Among hybrid methods, our method shows improved accuracy over the state-of-the-art on the combined dataset and the ICVL dataset that contain multiple subjects. Also, our algorithm is demonstrated to work well with unseen images.

Connected through hyperedge HE-1 (induced by source paper "structure from articulated motion accurate and stable monocular 3d reconstruction without training data"):
  with members:
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-2 (induced by source paper "an end to end framework for unconstrained monocular 3d hand pose estimation"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "learning joint reconstruction of hands and manipulated objects"
    - "3d hand shape and pose from images in the wild"
    - "single image 3d hand reconstruction with mesh convolutions"
    - "analysis of hand segmentation in the wild"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"

Connected through hyperedge HE-3 (induced by source paper "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "exploiting temporal information for 3d human pose estimation"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-4 (induced by source paper "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"

Question: Please tell me which class the center node 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.HC(Human-Computer Interaction)