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 learning of shape and pose with differentiable point clouds"
  Abstract: We address the problem of learning accurate 3D shape and camera pose from a collection of unlabeled category-specific images. We train a convolutional network to predict both the shape and the pose from a single image by minimizing the reprojection error: given several views of an object, the projections of the predicted shapes to the predicted camera poses should match the provided views. To deal with pose ambiguity, we introduce an ensemble of pose predictors which we then distill to a single "student" model. To allow for efficient learning of high-fidelity shapes, we represent the shapes by point clouds and devise a formulation allowing for differentiable projection of these. Our experiments show that the distilled ensemble of pose predictors learns to estimate the pose accurately, while the point cloud representation allows to predict detailed shape models. The supplementary video can be found at this https URL

Connected through hyperedge HE-1 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion"):
  with members:
    - "the virtual tailor predicting clothing in 3d as a function of human pose shape and garment style"
    - "tex2shape detailed full human body geometry from a single image"
    - "geometric disentanglement for generative latent shape models"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "360 degree textures of people in clothing from a single image"

Connected through hyperedge HE-2 (induced by source paper "sequential forecasting of 100 000 points"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "conditional invertible flow for point cloud generation"
    - "grip graph based interaction aware trajectory prediction"
    - "probabilistic reconstruction networks for 3d shape inference from a single image"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "human motion trajectory prediction a survey"
    - "pixor real time 3d object detection from point clouds"
    - "a baseline for 3d multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "render4completion synthesizing multi view depth maps for 3d shape completion"
    - "texture fields learning texture representations in function space"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "what do single view 3d reconstruction networks learn"

Connected through hyperedge HE-4 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency"):
  with members:
    - "photo geometric autoencoding to learn 3d objects from unlabelled images"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "deep mesh reconstruction from single rgb images via topology modification networks"
    - "scops self supervised co part segmentation"
    - "learning to infer implicit surfaces without 3d supervision"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "learning view priors for single view 3d reconstruction"

Connected through hyperedge HE-5 (induced by source paper "learning depth via interaction"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"

Connected through hyperedge HE-6 (induced by source paper "monocular neural image based rendering with continuous view control"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "transformable bottleneck networks"
    - "deepview view synthesis with learned gradient descent"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "visual object networks image generation with disentangled 3d representation"

Connected through hyperedge HE-7 (induced by source paper "hologan unsupervised learning of 3d representations from natural images"):
  with members:
    - "representation learning by rotating your faces"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "spectral normalization for generative adversarial networks"
    - "towards open set identity preserving face synthesis"

Connected through hyperedge HE-8 (induced by source paper "a neural rendering framework for free viewpoint relighting"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "visual representation learning with 3d view contrastive inverse graphics networks"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "monet unsupervised scene decomposition and representation"
    - "scene representation networks continuous 3d structure aware neural scene representations"
    - "neural point based graphics"

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)