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: "novel views of objects from a single image"
  Abstract: Taking an image of an object is at its core a lossy process. The rich information about the three-dimensional structure of the world is flattened to an image plane and decisions such as viewpoint and camera parameters are final and not easily revertible. As a consequence, possibilities of changing viewpoint are limited. Given a single image depicting an object, novel-view synthesis is the task of generating new images that render the object from a different viewpoint than the one given. The main difficulty is to synthesize the parts that are disoccluded; disocclusion occurs when parts of an object are hidden by the object itself under a specific viewpoint. In this work, we show how to improve novel-view synthesis by making use of the correlations observed in 3D models and applying them to new image instances. We propose a technique to use the structural information extracted from a 3D model that matches the image object in terms of viewpoint and shape. For the latter part, we propose an efficient 2D-to-3D alignment method that associates precisely the image appearance with the 3D model geometry with minimal user interaction. Our technique is able to simulate plausible viewpoint changes for a variety of object classes within seconds. Additionally, we show that our synthesized images can be used as additional training data that improves the performance of standard object detectors.

Connected through hyperedge HE-1 (induced by source paper "3d ken burns effect from a single image"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "transformable bottleneck networks"
    - "neural point based graphics"
    - "learning the depths of moving people by watching frozen people"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "neural rerendering in the wild"

Connected through hyperedge HE-2 (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-3 (induced by source paper "texture fields learning texture representations in function space"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "learning category specific mesh reconstruction from image collections"
    - "neural processes"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "warp and learn novel views generation for vehicles and other objects"):
  with members:
    - "open3d a modern library for 3d data processing"
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "semi parametric image synthesis"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "generative image inpainting with contextual attention"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "texturegan controlling deep image synthesis with texture patches"

Connected through hyperedge HE-5 (induced by source paper "itergans iterative gans to learn and control 3d object transformation"):
  with members:
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "deep hashing network for unsupervised domain adaptation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "face aging with conditional generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-6 (induced by source paper "photoshape photorealistic materials for large scale shape collections"):
  with members:
    - "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "joint material and illumination estimation from photo sets in the wild"
    - "material editing using a physically based rendering network"
    - "photographic image synthesis with cascaded refinement networks"
    - "a lightweight approach for on the fly reflectance estimation"
    - "image to image translation with conditional adversarial networks"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-7 (induced by source paper "synthesizing images of humans in unseen poses"):
  with members:
    - "deep view morphing"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "inferring and executing programs for visual reasoning"
    - "pose guided person image generation"
    - "a generative model of people in clothing"
    - "multi view image generation from a single view"
    - "convolutional pose machines"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"

Connected through hyperedge HE-8 (induced by source paper "siclope silhouette based clothed people"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "synthesizing images of humans in unseen poses"
    - "bodynet volumetric inference of 3d human body shapes"
    - "video based reconstruction of 3d people models"
    - "photo wake up 3d character animation from a single photo"
    - "view extrapolation of human body from a single image"
    - "3d human pose estimation in the wild by adversarial learning"

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)