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: "dense human body correspondences using convolutional networks"
  Abstract: We propose a deep learning approach for finding dense correspondences between 3D scans of people. Our method requires only partial geometric information in the form of two depth maps or partial reconstructed surfaces, works for humans in arbitrary poses and wearing any clothing, does not require the two people to be scanned from similar viewpoints, and runs in real time. We use a deep convolutional neural network to train a feature descriptor on depth map pixels, but crucially, rather than training the network to solve the shape correspondence problem directly, we train it to solve a body region classification problem, modified to increase the smoothness of the learned descriptors near region boundaries. This approach ensures that nearby points on the human body are nearby in feature space, and vice versa, rendering the feature descriptor suitable for computing dense correspondences between the scans. We validate our method on real and synthetic data for both clothed and unclothed humans, and show that our correspondences are more robust than is possible with state-of-the-art unsupervised methods, and more accurate than those found using methods that require full watertight 3D geometry.

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 "the whole is greater than the sum of its nonrigid parts"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"
    - "meteornet deep learning on dynamic 3d point cloud sequences"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-3 (induced by source paper "mgcn descriptor learning using multiscale gcns"):
  with members:
    - "graph wavelet neural network"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "dynamic graph cnn for learning on point clouds"
    - "learning compact geometric features"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "geometric deep learning going beyond euclidean data"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "dual grid net hand mesh vertex regression from single depth maps"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "end to end hand mesh recovery from a monocular rgb image"
    - "3d hand shape and pose from images in the wild"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning pose specific representations by predicting different views"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-5 (induced by source paper "learning shape templates with structured implicit functions"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "learning category specific mesh reconstruction from image collections"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "parsing geometry using structure aware shape templates"
    - "occupancy networks learning 3d reconstruction in function space"

Connected through hyperedge HE-6 (induced by source paper "moulding humans non parametric 3d human shape estimation from single images"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "group normalization"
    - "image based synthesis for deep 3d human pose estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "volumetric performance capture from minimal camera viewpoints"
    - "densepose dense human pose estimation in the wild"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-7 (induced by source paper "lbs autoencoder self supervised fitting of articulated meshes to point clouds"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "3d coded 3d correspondences by deep deformation"
    - "deep sets"
    - "unsupervised training for 3d morphable model regression"
    - "point cloud gan"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-8 (induced by source paper "unsupervised cycle consistent deformation for shape matching"):
  with members:
    - "shape2motion joint analysis of motion parts and attributes from 3d shapes"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "open3d a modern library for 3d data processing"
    - "continuous and orientation preserving correspondences via functional maps"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"

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)