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: "silnet single and multi view reconstruction by learning from silhouettes"
  Abstract: The objective of this paper is 3D shape understanding from single and multiple images. To this end, we introduce a new deep-learning architecture and loss function, SilNet, that can handle multiple views in an order-agnostic manner. The architecture is fully convolutional, and for training we use a proxy task of silhouette prediction, rather than directly learning a mapping from 2D images to 3D shape as has been the target in most recent work. #R##N#We demonstrate that with the SilNet architecture there is generalisation over the number of views -- for example, SilNet trained on 2 views can be used with 3 or 4 views at test-time; and performance improves with more views. #R##N#We introduce two new synthetics datasets: a blobby object dataset useful for pre-training, and a challenging and realistic sculpture dataset; and demonstrate on these datasets that SilNet has indeed learnt 3D shape. Finally, we show that SilNet exceeds the state of the art on the ShapeNet benchmark dataset, and use SilNet to generate novel views of the sculpture dataset.

Connected through hyperedge HE-1 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "attention based deep multiple instance learning"
    - "deepmvs learning multi view stereopsis"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "deep sets"
    - "deep imbalanced attribute classification using visual attention aggregation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "probabilistic reconstruction networks for 3d shape inference from a single image"):
  with members:
    - "on the convergence of adam and beyond"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-4 (induced by source paper "learning single image 3d reconstruction by generative modelling of shape pose and shading"):
  with members:
    - "structure aware shape synthesis"
    - "visual object networks image generation with disentangled 3d representation"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "multiresolution tree networks for 3d point cloud processing"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-5 (induced by source paper "what do single view 3d reconstruction networks learn"):
  with members:
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "learning category specific mesh reconstruction from image collections"
    - "open3d a modern library for 3d data processing"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "megadepth learning single view depth prediction from internet photos"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-6 (induced by source paper "multiview aggregation for learning category specific shape reconstruction"):
  with members:
    - "equivariant multi view networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "learning category specific mesh reconstruction from image collections"
    - "deepmvs learning multi view stereopsis"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-7 (induced by source paper "inverse graphics unsupervised learning of 3d shapes from single images"):
  with members:
    - "bridging the elbo and mmd"
    - "preventing posterior collapse with delta vaes"
    - "structure aware shape synthesis"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "3d prnn generating shape primitives with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "flexible svbrdf capture with a multi image deep network"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "deep sets"
    - "single image svbrdf capture with a rendering aware deep network"
    - "ps fcn a flexible learning framework for photometric stereo"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "material editing using a physically based rendering network"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

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)