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: "exploiting symmetry and or manhattan properties for 3d object structure estimation from single and multiple images"
  Abstract: Many man-made objects have intrinsic symmetries and Manhattan structure. By assuming an orthographic projection model, this paper addresses the estimation of 3D structures and camera projection using symmetry and/or Manhattan structure cues, which occur when the input is single- or multiple-image from the same category, e.g., multiple different cars. Specifically, analysis on the single image case implies that Manhattan alone is sufficient to recover the camera projection, and then the 3D structure can be reconstructed uniquely exploiting symmetry. However, Manhattan structure can be difficult to observe from a single image due to occlusion. To this end, we extend to the multiple-image case which can also exploit symmetry but does not require Manhattan axes. We propose a novel rigid structure from motion method, exploiting symmetry and using multiple images from the same category as input. Experimental results on the Pascal3D+ dataset show that our method significantly outperforms baseline methods.

Connected through hyperedge HE-1 (induced by source paper "exploring partial intrinsic and extrinsic symmetry in 3d medical imaging"):
  with members:
    - "x ray transform invariant anatomical landmark detection for pelvic trauma surgery"
    - "detecting approximate reflection symmetry in a point set using optimization on manifold"
    - "beyond planar symmetry modeling human perception of reflection and rotation symmetries in the wild"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"):
  with members:
    - "fusion volumetric object level slam"
    - "interactive perception leveraging action in perception and perception in action"
    - "shape completion enabled robotic grasping"
    - "analysis and observations from the first amazon picking challenge"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "a dataset for improved rgbd based object detection and pose estimation for warehouse pick and place"
    - "deep multimodal embedding manipulating novel objects with point clouds language and trajectories"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"

Connected through hyperedge HE-5 (induced by source paper "craves controlling robotic arm with a vision based economic system"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "the costar block stacking dataset learning with workspace constraints"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

Connected through hyperedge HE-6 (induced by source paper "end to end 3d shape inverse rendering of different classes of objects from a single input image"):
  with members:
    - "3d face reconstruction with geometry details from a single image"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "interactive 3d modeling with a generative adversarial network"
    - "from images to 3d shape attributes"

Connected through hyperedge HE-7 (induced by source paper "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"):
  with members:
    - "symmetric non rigid structure from motion for category specific object structure estimation"
    - "sparse illumination learning and transfer for single sample face recognition with image corruption and misalignment"
    - "constructing a non negative low rank and sparse graph with data adaptive features"
    - "deeply learned face representations are sparse selective and robust"
    - "dynamic updating for ell_ 1 minimization"

Connected through hyperedge HE-8 (induced by source paper "reconstructing vechicles from a single image shape priors for road scene understanding"):
  with members:
    - "towards scene understanding with detailed 3d object representations"
    - "flowing convnets for human pose estimation in videos"
    - "viewpoints and keypoints"
    - "caffe convolutional architecture for fast feature embedding"

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)