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: "multilinear wavelets a statistical shape space for human faces"
  Abstract: We present a statistical model for $3$D human faces in varying expression, which decomposes the surface of the face using a wavelet transform, and learns many localized, decorrelated multilinear models on the resulting coefficients. Using this model we are able to reconstruct faces from noisy and occluded $3$D face scans, and facial motion sequences. Accurate reconstruction of face shape is important for applications such as tele-presence and gaming. The localized and multi-scale nature of our model allows for recovery of fine-scale detail while retaining robustness to severe noise and occlusion, and is computationally efficient and scalable. We validate these properties experimentally on challenging data in the form of static scans and motion sequences. We show that in comparison to a global multilinear model, our model better preserves fine detail and is computationally faster, while in comparison to a localized PCA model, our model better handles variation in expression, is faster, and allows us to fix identity parameters for a given subject.

Connected through hyperedge HE-1 (induced by source paper "meshgan non linear 3d morphable models of faces"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "inverting the generator of a generative adversarial network ii"
    - "relational inductive biases deep learning and graph networks"
    - "escaping from collapsing modes in a constrained space"
    - "a simple approach to intrinsic correspondence learning on unstructured 3d meshes"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "nonlinear 3d face morphable model"
    - "variational autoencoders for deforming 3d mesh models"

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"
    - "dense 3d face correspondence"

Connected through hyperedge HE-3 (induced by source paper "towards high fidelity nonlinear 3d face morphable model"):
  with members:
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "extreme 3d face reconstruction looking past occlusions"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"

Connected through hyperedge HE-4 (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-5 (induced by source paper "learning disentangled representations via independent subspaces"):
  with members:
    - "structuring autoencoders"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "mask guided portrait editing with conditional gans"
    - "uncalibrated non rigid factorisation by independent subspace analysis"
    - "markov chain neural networks"
    - "generating a fusion image one s identity and another s shape"
    - "attgan facial attribute editing by only changing what you want"

Connected through hyperedge HE-6 (induced by source paper "generating 3d faces using convolutional mesh autoencoders"):
  with members:
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "deformable shape completion with graph convolutional autoencoders"
    - "extreme 3d face reconstruction looking past occlusions"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "geometric deep learning going beyond euclidean data"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "learning shape correspondence with anisotropic convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "sketch based face editing in video using identity deformation transfer"):
  with members:
    - "cr gan learning complete representations for multi view generation"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "mocogan decomposing motion and content for video generation"
    - "image to image translation with conditional adversarial networks"
    - "sketch based 3d shape retrieval using convolutional neural networks"

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)