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: "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
  Abstract: The long-coveted task of reconstructing 3D geometry from images is still a standing problem. In this paper, we build on the power of neural networks and introduce Pix2Vex, a network trained to convert camera-captured images into 3D geometry. We present a novel differentiable renderer ($DR$) as a forward validation means during training. Our key insight is that $DR$s produce images of a particular appearance, different from typical input images. Hence, we propose adding an image-to-image translation component, converting between these rendering styles. This translation closes the training loop, while allowing to use minimal supervision only, without needing any 3D model as ground truth. Unlike state-of-the-art methods, our $DR$ is $C^\infty$ smooth and thus does not display any discontinuities at occlusions or dis-occlusions. Through our novel training scheme, our network can train on different types of images, where previous work can typically only train on images of a similar appearance to those rendered by a $DR$.

Connected through hyperedge HE-1 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "differentiable surface splatting for point based geometry processing"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "3d local features for direct pairwise registration"
    - "view n gram network for 3d object retrieval"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "pytorch an imperative style high performance deep learning library"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-2 (induced by source paper "differentiable volumetric rendering learning implicit 3d representations without 3d supervision"):
  with members:
    - "pix2vox context aware 3d reconstruction from single and multi view images"
    - "learning shape templates with structured implicit functions"
    - "controlling neural level sets"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "texture fields learning texture representations in function space"
    - "kpconv flexible and deformable convolution for point clouds"

Connected through hyperedge HE-3 (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"
    - "monet unsupervised scene decomposition and representation"
    - "scene representation networks continuous 3d structure aware neural scene representations"
    - "neural point based graphics"
    - "inverse path tracing for joint material and lighting estimation"

Connected through hyperedge HE-4 (induced by source paper "dist rendering deep implicit signed distance function with differentiable sphere tracing"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "differentiable surface splatting for point based geometry processing"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "texture fields learning texture representations in function space"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "dpsnet end to end deep plane sweep stereo"
    - "neural volumes learning dynamic renderable volumes from images"

Connected through hyperedge HE-5 (induced by source paper "sdfdiff differentiable rendering of signed distance fields for 3d shape optimization"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "visual object networks image generation with disentangled 3d representation"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-6 (induced by source paper "differentiable surface splatting for point based geometry processing"):
  with members:
    - "render4completion synthesizing multi view depth maps for 3d shape completion"
    - "pointcleannet learning to denoise and remove outliers from dense point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "inverse path tracing for joint material and lighting estimation"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "3d aware scene manipulation via inverse graphics"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "ec net an edge aware point set consolidation network"

Connected through hyperedge HE-7 (induced by source paper "learning to predict 3d objects with an interpolation based differentiable renderer"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "what do single view 3d reconstruction networks learn"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "learning category specific mesh reconstruction from image collections"
    - "3d aware scene manipulation via inverse graphics"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-8 (induced by source paper "neural view interpolation for sparse light field video"):
  with members:
    - "deepview view synthesis with learned gradient descent"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "neural volumes learning dynamic renderable volumes from images"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "stereo magnification learning view synthesis using multiplane images"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "sampling generative networks"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

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)