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: "shape retrieval of non rigid 3d human models"
  Abstract: 3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods.

Connected through hyperedge HE-1 (induced by source paper "geometric disentanglement for generative latent shape models"):
  with members:
    - "a two step disentanglement method"
    - "learning disentangled representations with reference based variational autoencoders"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "a simple probabilistic deep generative model for learning generalizable disentangled representations from grouped data"
    - "hyperprior induced unsupervised disentanglement of latent representations"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "adaptive template systems data driven feature selection for learning with persistence diagrams"):
  with members:
    - "approximating continuous functions on persistence diagrams using template functions"
    - "supervised learning of labeled pointcloud differences via cover tree entropy reduction"
    - "a persistence landscapes toolbox for topological statistics"

Connected through hyperedge HE-3 (induced by source paper "adaptive partitioning for template functions on persistence diagrams"):
  with members:
    - "approximating continuous functions on persistence diagrams using template functions"
    - "sliced wasserstein kernel for persistence diagrams"

Connected through hyperedge HE-4 (induced by source paper "farm functional automatic registration method for 3d human bodies"):
  with members:
    - "multilevel active registration for kinect human body scans from low quality to high quality"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "building statistical shape spaces for 3d human modeling"
    - "deformable shape completion with graph convolutional autoencoders"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "localized manifold harmonics for spectral shape analysis"
    - "sparse modeling of intrinsic correspondences"

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)