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: "fully automatic expression invariant face correspondence"
  Abstract: We consider the problem of computing accurate point-to-point correspondences among a set of human face scans with varying expressions. Our fully automatic approach does not require any manually placed markers on the scan. Instead, the approach learns the locations of a set of landmarks present in a database and uses this knowledge to automatically predict the locations of these landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. To accurately fit the expression of the template to the expression of the scan, we use as template a blendshape model. Our algorithm was tested on a database of human faces of different ethnic groups with strongly varying expressions. Experimental results show that the obtained point-to-point correspondence is both highly accurate and consistent for most of the tested 3D face models.

Connected through hyperedge HE-1 (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"
    - "dense 3d face correspondence"

Connected through hyperedge HE-2 (induced by source paper "a decoupled 3d facial shape model by adversarial training"):
  with members:
    - "synthesizing facial photometries and corresponding geometries using generative adversarial networks"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "ganimation anatomically aware facial animation from a single image"
    - "high quality facial surface and texture synthesis via generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "improved training of wasserstein gans"
    - "uv gan adversarial facial uv map completion for pose invariant face recognition"

Connected through hyperedge HE-3 (induced by source paper "multi view consensus cnn for 3d facial landmark placement"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "geometric deep learning going beyond euclidean data"
    - "silnet single and multi view reconstruction by learning from silhouettes"
    - "stacked hourglass networks for human pose estimation"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "orientation boosted voxel nets for 3d object recognition"
    - "learning shape correspondence with anisotropic convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "dense 3d face correspondence"):
  with members:
    - "rotational projection statistics for 3d local surface description and object recognition"

Connected through hyperedge HE-5 (induced by source paper "multilinear wavelets a statistical shape space for human faces"):
  with members:
    - "review of statistical shape spaces for 3d data with comparative analysis for human faces"

Connected through hyperedge HE-6 (induced by source paper "review of statistical shape spaces for 3d data with comparative analysis for human faces"):
  with members:
    - "estimating 3d human shapes from measurements"

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)