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: "building statistical shape spaces for 3d human modeling"
  Abstract: Expressive 3D human shape models are proposed.The models are learned from the largest available dataset of laser scans.Various template fitting and posture normalization approaches are evaluated.High quality of the learned shape spaces is empirically demonstrated.Proposed models and code to data pre-processing and model fitting are released. Statistical models of 3D human shape and pose learned from scan databases have developed into valuable tools to solve a variety of vision and graphics problems. Unfortunately, most publicly available models are of limited expressiveness as they were learned on very small databases that hardly reflect the true variety in human body shapes. In this paper, we contribute by rebuilding a widely used statistical body representation from the largest commercially available scan database, and making the resulting model available to the community (visit http://humanshape.mpi-inf.mpg.de). As preprocessing several thousand scans for learning the model is a challenge in itself, we contribute by developing robust best practice solutions for scan alignment that quantitatively lead to the best learned models. We make implementations of these preprocessing steps also publicly available. We extensively evaluate the improved accuracy and generality of our new model, and show its improved performance for human body reconstruction from sparse input data.

Connected through hyperedge HE-1 (induced by source paper "calvis chest waist and pelvis circumference from 3d human body meshes as ground truth for deep learning"):
  with members:
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "end to end recovery of human shape and pose"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "learning from synthetic humans"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "estimating 3d human shapes from measurements"

Connected through hyperedge HE-2 (induced by source paper "rank3dgan semantic mesh generation using relative attributes"):
  with members:
    - "multi chart generative surface modeling"
    - "learning category specific mesh reconstruction from image collections"
    - "ranking cgans subjective control over semantic image attributes"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

Connected through hyperedge HE-3 (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-4 (induced by source paper "deep structured implicit functions"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "mesh r cnn"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"

Connected through hyperedge HE-5 (induced by source paper "latent feature disentanglement for 3d meshes"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "exploring disentangled feature representation beyond face identification"
    - "dgpose disentangled semi supervised deep generative models for human body analysis"
    - "everybody dance now"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-6 (induced by source paper "learning 3d human body embedding"):
  with members:
    - "disentangled representation learning for 3d face shape"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "video based reconstruction of 3d people models"
    - "alive caricature from 2d to 3d"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"

Connected through hyperedge HE-7 (induced by source paper "concise and effective network for 3d human modeling from orthogonal silhouettes"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "focal loss for dense object detection"
    - "end to end recovery of human shape and pose"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "zernet convolutional neural networks on arbitrary surfaces via zernike local tangent space estimation"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "meshcnn a network with an edge"
    - "parallel transport convolution a new tool for convolutional neural networks on manifolds"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "dynamic graph cnn for learning on point clouds"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "deep spectral descriptors learning the point wise correspondence metric via siamese deep neural networks"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"

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)