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: "pifu pixel aligned implicit function for high resolution clothed human digitization"
  Abstract: We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu can produce high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.

Connected through hyperedge HE-1 (induced by source paper "learning to transfer texture from clothing images to 3d humans"):
  with members:
    - "the virtual tailor predicting clothing in 3d as a function of human pose shape and garment style"
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "tex2shape detailed full human body geometry from a single image"
    - "unsupervised person image generation with semantic parsing transformation"
    - "learning to dress 3d people in generative clothing"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "360 degree textures of people in clothing from a single image"
    - "multi garment net learning to dress 3d people from images"

Connected through hyperedge HE-2 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "learning shape templates with structured implicit functions"
    - "controlling neural level sets"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "escaping plato s cave 3d shape from adversarial rendering"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "towards unsupervised learning of generative models for 3d controllable image synthesis"

Connected through hyperedge HE-3 (induced by source paper "learning nonparametric human mesh reconstruction from a single image without ground truth meshes"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d scene reconstruction with multi layer depth and epipolar transformers"
    - "texturepose supervising human mesh estimation with texture consistency"
    - "cross domain complementary learning with synthetic data for multi person part segmentation"
    - "end to end hand mesh recovery from a monocular rgb image"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "delving deep into hybrid annotations for 3d human recovery in the wild"
    - "detailed human shape estimation from a single image by hierarchical mesh deformation"

Connected through hyperedge HE-4 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "deep video based performance cloning"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "video based reconstruction of 3d people models"
    - "sfv reinforcement learning of physical skills from videos"

Connected through hyperedge HE-5 (induced by source paper "learning from unlabelled videos using contrastive predictive neural 3d mapping"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "learning spatial common sense with geometry aware recurrent networks"
    - "learning to decompose and disentangle representations for video prediction"
    - "representation learning with contrastive predictive coding"
    - "learning a multi view stereo machine"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-6 (induced by source paper "peelnet textured 3d reconstruction of human body using single view rgb image"):
  with members:
    - "humanmeshnet polygonal mesh recovery of humans"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "graphonomy universal human parsing via graph transfer learning"
    - "detailed human shape estimation from a single image by hierarchical mesh deformation"
    - "layer structured 3d scene inference via view synthesis"
    - "3d human body reconstruction from a single image via volumetric regression"
    - "siclope silhouette based clothed people"

Connected through hyperedge HE-7 (induced by source paper "dressing 3d humans using a conditional mesh vae gan"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "meshgan non linear 3d morphable models of faces"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"

Connected through hyperedge HE-8 (induced by source paper "360 degree textures of people in clothing from a single image"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "textured neural avatars"
    - "unsupervised part based disentangling of object shape and appearance"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "pose guided human video generation"
    - "synthesizing images of humans in unseen poses"
    - "coordinate based texture inpainting for pose guided image generation"

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)