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: "sparse data driven mesh deformation"
  Abstract: Example-based mesh deformation methods are powerful tools for realistic shape editing. However, existing techniques typically combine all the example deformation modes, which can lead to overfitting, i.e. using a overly complicated model to explain the user-specified deformation. This leads to implausible or unstable deformation results, including unexpected global changes outside the region of interest. To address this fundamental limitation, we propose a sparse blending method that automatically selects a smaller number of deformation modes to compactly describe the desired deformation. This along with a suitably chosen deformation basis including spatially localized deformation modes leads to significant advantages, including more meaningful, reliable, and efficient deformations because fewer and localized deformation modes are applied. To cope with large rotations, we develop a simple but effective representation based on polar decomposition of deformation gradients, which resolves the ambiguity of large global rotations using an as-consistent-as-possible global optimization. This simple representation has a closed form solution for derivatives, making it efficient for sparse localized representation and thus ensuring interactive performance. Experimental results show that our method outperforms state-of-the-art data-driven mesh deformation methods, for both quality of results and efficiency.

Connected through hyperedge HE-1 (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"
    - "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"
    - "building statistical shape spaces for 3d human modeling"

Connected through hyperedge HE-2 (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"
    - "variational autoencoders for deforming 3d mesh models"
    - "decomposing motion and content for natural video sequence prediction"
    - "building statistical shape spaces for 3d human modeling"

Connected through hyperedge HE-3 (induced by source paper "mesh variational autoencoders with edge contraction pooling"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "variational autoencoders for deforming 3d mesh models"
    - "deformable shape completion with graph convolutional autoencoders"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-4 (induced by source paper "disentangled representation learning for 3d face shape"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "alive caricature from 2d to 3d"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "nonlinear 3d face morphable model"
    - "3d face morphable models in the wild"
    - "geometric deep learning going beyond euclidean data"
    - "mesh based autoencoders for localized deformation component analysis"

Connected through hyperedge HE-5 (induced by source paper "realtime simulation of thin shell deformable materials using cnn based mesh embedding"):
  with members:
    - "fast motion planning for high dof robot systems using hierarchical system identification"
    - "hierarchical cloth simulation using deep neural networks"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "cloth manipulation using random forest based controller parametrization"
    - "variational autoencoders for deforming 3d mesh models"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "mesh based autoencoders for localized deformation component analysis"

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"
    - "variational autoencoders for deforming 3d mesh models"
    - "building statistical shape spaces for 3d human modeling"

Connected through hyperedge HE-7 (induced by source paper "learning bidirectional lstm networks for synthesizing 3d mesh animation sequences"):
  with members:
    - "deep video generation prediction and completion of human action sequences"
    - "variational autoencoders for deforming 3d mesh models"
    - "deep representation learning for human motion prediction and classification"
    - "geometric deep learning going beyond euclidean data"
    - "on human motion prediction using recurrent neural networks"
    - "the pose knows video forecasting by generating pose futures"
    - "auto conditioned recurrent networks for extended complex human motion synthesis"
    - "3d shape segmentation with projective convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "alive caricature from 2d to 3d"):
  with members:
    - "3d face reconstruction with geometry details from a single image"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "webcaricature a benchmark for caricature face recognition"

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.GR(Graphics)