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: "unsupervised adversarial learning of 3d human pose from 2d joint locations"
  Abstract: The task of three-dimensional (3D) human pose estimation from a single image can be divided into two parts: (1) Two-dimensional (2D) human joint detection from the image and (2) estimating a 3D pose from the 2D joints. Herein, we focus on the second part, i.e., a 3D pose estimation from 2D joint locations. The problem with existing methods is that they require either (1) a 3D pose dataset or (2) 2D joint locations in consecutive frames taken from a video sequence. We aim to solve these problems. For the first time, we propose a method that learns a 3D human pose without any 3D datasets. Our method can predict a 3D pose from 2D joint locations in a single image. Our system is based on the generative adversarial networks, and the networks are trained in an unsupervised manner. Our primary idea is that, if the network can predict a 3D human pose correctly, the 3D pose that is projected onto a 2D plane should not collapse even if it is rotated perpendicularly. We evaluated the performance of our method using Human3.6M and the MPII dataset and showed that our network can predict a 3D pose well even if the 3D dataset is not available during training.

Connected through hyperedge HE-1 (induced by source paper "chained representation cycling learning to estimate 3d human pose and shape by cycling between representations"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "unsupervised learning of object structure and dynamics from videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "ordinal depth supervision for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-2 (induced by source paper "deep nrsfm towards 3d reconstruction in the wild"):
  with members:
    - "distill knowledge from nrsfm for weakly supervised 3d pose learning"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "deep high resolution representation learning for human pose estimation"
    - "deep non rigid structure from motion"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-3 (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"
    - "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"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"

Connected through hyperedge HE-4 (induced by source paper "photo geometric autoencoding to learn 3d objects from unlabelled images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"
    - "an adversarial neuro tensorial approach for learning disentangled representations"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "learning view priors for single view 3d reconstruction"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"

Connected through hyperedge HE-5 (induced by source paper "c3dpo canonical 3d pose networks for non rigid structure from motion"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "deep high resolution representation learning for human pose estimation"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "learning 3d human dynamics from video"
    - "can 3d pose be learned from 2d projections alone"
    - "scalable dense non rigid structure from motion a grassmannian perspective"

Connected through hyperedge HE-6 (induced by source paper "learning body shape and pose from dense correspondences"):
  with members:
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "simple baselines for human pose estimation and tracking"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "skeleton transformer networks 3d human pose and skinned mesh from single rgb image"
    - "densepose dense human pose estimation in the wild"
    - "end to end recovery of human shape and pose"

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)