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: "3d human pose estimation using convolutional neural networks with 2d pose information"
  Abstract: While there has been a success in 2D human pose estimation with convolutional neural networks (CNNs), 3D human pose estimation has not been thoroughly studied. In this paper, we tackle the 3D human pose estimation task with end-to-end learning using CNNs. Relative 3D positions between one joint and the other joints are learned via CNNs. The proposed method improves the performance of CNN with two novel ideas. First, we added 2D pose information to estimate a 3D pose from an image by concatenating 2D pose estimation result with the features from an image. Second, we have found that more accurate 3D poses are obtained by combining information on relative positions with respect to multiple joints, instead of just one root joint. Experimental results show that the proposed method achieves comparable performance to the state-of-the-art methods on Human 3.6m dataset.

Connected through hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation"):
  with members:
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "cross view fusion for 3d human pose estimation"
    - "learnable triangulation of human pose"
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "billion scale semi supervised learning for image classification"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "texturepose supervising human mesh estimation with texture consistency"

Connected through hyperedge HE-2 (induced by source paper "hemlets posh learning part centric heatmap triplets for 3d human pose and shape estimation"):
  with members:
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "denserac joint 3d pose and shape estimation by dense render and compare"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "hemlets pose learning part centric heatmap triplets for accurate 3d human pose estimation"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"

Connected through hyperedge HE-3 (induced by source paper "lightweight 3d human pose estimation network training using teacher student learning"):
  with members:
    - "learnable triangulation of human pose"
    - "improved training of binary networks for human pose estimation and image recognition"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "deep high resolution representation learning for human pose estimation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "ordinal depth supervision for 3d human pose estimation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "anatomy aware 3d human pose estimation in videos"):
  with members:
    - "chirality nets for human pose regression"
    - "trajectory space factorization for deep video based 3d human pose estimation"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "generating multiple hypotheses for 3d human pose estimation with mixture density network"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "learning 3d human dynamics from video"

Connected through hyperedge HE-5 (induced by source paper "camera distance aware top down approach for 3d multi person pose estimation from a single rgb image"):
  with members:
    - "multi scale aggregation r cnn for 2d multi person pose estimation"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "deep attention based classification network for robust depth prediction"
    - "posefix model agnostic general human pose refinement network"
    - "deep ordinal regression network for monocular depth estimation"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "3d human pose estimation in the wild by adversarial learning"

Connected through hyperedge HE-6 (induced by source paper "unsupervised 3d pose estimation with geometric self supervision"):
  with members:
    - "learning to generate synthetic data via compositing"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised adversarial learning of 3d human pose from 2d joint locations"
    - "3d human pose estimation with relational networks"
    - "3d human pose estimation in the wild by adversarial learning"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "can 3d pose be learned from 2d projections alone"
    - "orinet a fully convolutional network for 3d human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "xr egopose egocentric 3d human pose from an hmd camera"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "can 3d pose be learned from 2d projections alone"
    - "exploiting temporal information for 3d human pose estimation"
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"

Connected through hyperedge HE-8 (induced by source paper "a unified deep framework for joint 3d pose estimation and action recognition from a single rgb camera"):
  with members:
    - "skeletal movement to color map a novel representation for 3d action recognition with inception residual networks"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "efficient neural architecture search via parameter sharing"
    - "exploiting deep residual networks for human action recognition from skeletal data"
    - "skeleton based human action recognition with global context aware attention lstm networks"

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)