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: "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
  Abstract: We propose a CNN-based approach for multi-camera markerless motion capture of the human body. Unlike existing methods that first perform pose estimation on individual cameras and generate 3D models as post-processing, our approach makes use of 3D reasoning throughout a multi-stage approach. This novelty allows us to use provisional 3D models of human pose to rethink where the joints should be located in the image and to recover from past mistakes. Our principled refinement of 3D human poses lets us make use of image cues, even from images where we previously misdetected joints, to refine our estimates as part of an end-to-end approach. Finally, we demonstrate how the high-quality output of our multi-camera setup can be used as an additional training source to improve the accuracy of existing single camera models.

Connected through hyperedge HE-1 (induced by source paper "cross view tracking for multi human 3d pose estimation at over 100 fps"):
  with members:
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "simple baselines for human pose estimation and tracking"
    - "exploiting temporal information for 3d human pose estimation"
    - "single shot multi person 3d pose estimation from monocular rgb"
    - "harvesting multiple views for marker less 3d human pose annotations"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning to refine human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "deepfuse an imu aware network for real time 3d human pose estimation from multi view image"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "a generalizable approach for multi view 3d human pose regression"
    - "multi scale structure aware network for human pose estimation"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation with 2d marginal heatmaps"
    - "numerical coordinate regression with convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "shape aware human pose and shape reconstruction using multi view images"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "fashion is taking shape understanding clothing preference based on body shape from online sources"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-5 (induced by source paper "u4d unsupervised 4d dynamic scene understanding"):
  with members:
    - "4d temporally coherent light field video"
    - "continuous 3d label stereo matching using local expansion moves"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "accurate optical flow via direct cost volume processing"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "towards accurate markerless human shape and pose estimation over time"
    - "joint multi person pose estimation and semantic part segmentation"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "learnable triangulation of human pose"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "a generalizable approach for multi view 3d human pose regression"
    - "humbi 1 0 human multiview behavioral imaging dataset"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"
    - "learning spatial common sense with geometry aware recurrent networks"
    - "exploiting temporal information for 3d human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "cross view fusion for 3d human pose estimation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "simple baselines for human pose estimation and tracking"
    - "flow guided feature aggregation for video object detection"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "end to end recovery of human shape and pose"
    - "harvesting multiple views for marker less 3d human pose annotations"

Connected through hyperedge HE-8 (induced by source paper "3d human pose estimation in video with temporal convolutions and semi supervised training"):
  with members:
    - "on the convergence of adam and beyond"
    - "ordinal depth supervision for 3d human pose estimation"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "can 3d pose be learned from 2d projections alone"
    - "exploiting temporal information for 3d human pose estimation"
    - "understanding back translation at scale"

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)