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 3d face correspondence"
  Abstract: We present an algorithm that automatically establishes dense correspondences between a large number of 3D faces. Starting from automatically detected sparse correspondences on the outer boundary of 3D faces, the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges. After exhausting keypoint matches, further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles. A deformable model (K3DM) is constructed from the dense corresponded faces and an algorithm is proposed for morphing the K3DM to fit unseen faces. This algorithm iterates between rigid alignment of an unseen face followed by regularized morphing of the deformable model. We have extensively evaluated the proposed algorithms on synthetic data and real 3D faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using quantitative and qualitative benchmarks. Our algorithm achieved dense correspondences with a mean localisation error of 1.28 mm on synthetic faces and detected 14 anthropometric landmarks on unseen real faces from the FRGCv2 database with 3 mm precision. Furthermore, our deformable model fitting algorithm achieved 98.5 percent face recognition accuracy on the FRGCv2 and 98.6 percent on Bosphorus database. Our dense model is also able to generalize to unseen datasets.

Connected through hyperedge HE-1 (induced by source paper "spherical kernel for efficient graph convolution on 3d point clouds"):
  with members:
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "so net self organizing network for point cloud analysis"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "learning to sample"
    - "tangent convolutions for dense prediction in 3d"

Connected through hyperedge HE-2 (induced by source paper "3d face modeling from diverse raw scan data"):
  with members:
    - "joint face alignment and 3d face reconstruction with application to face recognition"
    - "disentangled representation learning for 3d face shape"
    - "on learning 3d face morphable model from in the wild images"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning category specific mesh reconstruction from image collections"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "3d coded 3d correspondences by deep deformation"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-3 (induced by source paper "data free point cloud network for 3d face recognition"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "arcface additive angular margin loss for deep face recognition"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "learning from millions of 3d scans for large scale 3d face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (induced by source paper "face recognition from sequential sparse 3d data via deep registration"):
  with members:
    - "additive margin softmax for face verification"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning from millions of 3d scans for large scale 3d face recognition"
    - "faceposenet making a case for landmark free face alignment"
    - "deep 3d face identification"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-5 (induced by source paper "learning from millions of 3d scans for large scale 3d face recognition"):
  with members:
    - "end to end 3d face reconstruction with deep neural networks"
    - "toward open set face recognition"
    - "level playing field for million scale face recognition"
    - "deep 3d face identification"
    - "do we really need to collect millions of faces for effective face recognition"
    - "3d face reconstruction by learning from synthetic data"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-6 (induced by source paper "3d face reconstruction from light field images a model free approach"):
  with members:
    - "learning from millions of 3d scans for large scale 3d face recognition"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "3d face morphable models in the wild"
    - "densely connected convolutional networks"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"

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)