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: "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
  Abstract: Researchers have developed excellent feed-forward models that learn to map images to desired outputs, such as to the images' latent factors, or to other images, using supervised learning. Learning such mappings from unlabelled data, or improving upon supervised models by exploiting unlabelled data, remains elusive. We argue that there are two important parts to learning without annotations: (i) matching the predictions to the input observations, and (ii) matching the predictions to known priors. We propose Adversarial Inverse Graphics networks (AIGNs): weakly supervised neural network models that combine feedback from rendering their predictions, with distribution matching between their predictions and a collection of ground-truth factors. We apply AIGNs to 3D human pose estimation and 3D structure and egomotion estimation, and outperform models supervised by only paired annotations. We further apply AIGNs to facial image transformation using super-resolution and inpainting renderers, while deliberately adding biases in the ground-truth datasets. Our model seamlessly incorporates such biases, rendering input faces towards young, old, feminine, masculine or Tom Cruise-like equivalents (depending on the chosen bias), or adding lip and nose augmentations while inpainting concealed lips and noses.

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 "weakly supervised 3d human pose learning via multi view images 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"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "learning the depths of moving people by watching frozen people"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"

Connected through hyperedge HE-3 (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-4 (induced by source paper "learning landmarks from unaligned data using image translation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "learning to read by spelling towards unsupervised text recognition"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "self supervised learning of a facial attribute embedding from video"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "3d human pose estimation in the wild by adversarial learning"
    - "unsupervised discovery of object landmarks as structural representations"

Connected through hyperedge HE-5 (induced by source paper "unsupervised learning of probably symmetric deformable 3d objects from images in the wild"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "unsupervised generative 3d shape learning from natural images"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "unsupervised 3d pose estimation with geometric self supervision"

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"
    - "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"
    - "sparse representation for 3d shape estimation a convex relaxation approach"

Connected through hyperedge HE-8 (induced by source paper "monocular 3d object detection with pseudo lidar point cloud"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "3d object detection using scale invariant and feature reweighting networks"
    - "ipod intensive point based object detector for point cloud"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "groundnet segmentation aware monocular ground plane estimation with geometric consistency"
    - "deep ordinal regression network for monocular depth 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)