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: "im2avatar colorful 3d reconstruction from a single image"
  Abstract: Existing works on single-image 3D reconstruction mainly focus on shape recovery. In this work, we study a new problem, that is, simultaneously recovering 3D shape and surface color from a single image, namely "colorful 3D reconstruction". This problem is both challenging and intriguing because the ability to infer textured 3D model from a single image is at the core of visual understanding. Here, we propose an end-to-end trainable framework, Colorful Voxel Network (CVN), to tackle this problem. Conditioned on a single 2D input, CVN learns to decompose shape and surface color information of a 3D object into a 3D shape branch and a surface color branch, respectively. Specifically, for the shape recovery, we generate a shape volume with the state of its voxels indicating occupancy. For the surface color recovery, we combine the strength of appearance hallucination and geometric projection by concurrently learning a regressed color volume and a 2D-to-3D flow volume, which are then fused into a blended color volume. The final textured 3D model is obtained by sampling color from the blended color volume at the positions of occupied voxels in the shape volume. To handle the severe sparse volume representations, a novel loss function, Mean Squared False Cross-Entropy Loss (MSFCEL), is designed. Extensive experiments demonstrate that our approach achieves significant improvement over baselines, and shows great generalization across diverse object categories and arbitrary viewpoints.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a gamified simulator and physical platform for self driving algorithm training and validation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "beyond grand theft auto v for training testing and enhancing deep learning in self driving cars"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-3 (induced by source paper "delving deep into hybrid annotations for 3d human recovery in the wild"):
  with members:
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "simple baselines for human pose estimation and tracking"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "densepose dense human pose estimation in the wild"
    - "end to end recovery of human shape and pose"
    - "unite the people closing the loop between 3d and 2d human representations"

Connected through hyperedge HE-4 (induced by source paper "pointgrow autoregressively learned point cloud generation with self attention"):
  with members:
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "dynamic graph cnn for learning on point clouds"
    - "x vision an augmented vision tool with real time sensing ability in tagged environments"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "visualization and labeling of point clouds in virtual reality"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "learning representations and generative models for 3d point clouds"

Connected through hyperedge HE-5 (induced by source paper "visual object networks image generation with disentangled 3d representation"):
  with members:
    - "learning representations and generative models for 3d point clouds"
    - "toward multimodal image to image translation"
    - "instance normalization the missing ingredient for fast stylization"
    - "unsupervised learning of 3d structure from images"
    - "shapenet an information rich 3d model repository"

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)