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: "holistic planimetric prediction to local volumetric prediction for 3d human pose estimation"
  Abstract: We propose a novel approach to 3D human pose estimation from a single depth map. Recently, convolutional neural network (CNN) has become a powerful paradigm in computer vision. Many of computer vision tasks have benefited from CNNs, however, the conventional approach to directly regress 3D body joint locations from an image does not yield a noticeably improved performance. In contrast, we formulate the problem as estimating per-voxel likelihood of key body joints from a 3D occupancy grid. We argue that learning a mapping from volumetric input to volumetric output with 3D convolution consistently improves the accuracy when compared to learning a regression from depth map to 3D joint coordinates. We propose a two-stage approach to reduce the computational overhead caused by volumetric representation and 3D convolution: Holistic 2D prediction and Local 3D prediction. In the first stage, Planimetric Network (P-Net) estimates per-pixel likelihood for each body joint in the holistic 2D space. In the second stage, Volumetric Network (V-Net) estimates the per-voxel likelihood of each body joints in the local 3D space around the 2D estimations of the first stage, effectively reducing the computational cost. Our model outperforms existing methods by a large margin in publicly available datasets.

Connected through hyperedge HE-1 (induced by source paper "patch based 3d human pose refinement"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "ordinal depth supervision for 3d human pose estimation"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "posefix model agnostic general human pose refinement network"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-2 (induced by source paper "a2j anchor to joint regression network for 3d articulated pose estimation from a single depth image"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "deepprior improving fast and accurate 3d hand pose estimation"

Connected through hyperedge HE-3 (induced by source paper "3d human pose estimation in rgbd images for robotic task learning"):
  with members:
    - "learning to estimate 3d hand pose from single rgb images"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "convolutional pose machines"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "choosing smartly adaptive multimodal fusion for object detection in changing environments"

Connected through hyperedge HE-4 (induced by source paper "dense 3d regression for hand pose estimation"):
  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"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "deepprior improving fast and accurate 3d 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.CV(Computer Vision and Pattern Recognition)