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: "head reconstruction from internet photos"
  Abstract: 3D face reconstruction from Internet photos has recently produced exciting results. A person's face, e.g., Tom Hanks, can be modeled and animated in 3D from a completely uncalibrated photo collection. Most methods, however, focus solely on face area and mask out the rest of the head. This paper proposes that head modeling from the Internet is a problem we can solve. We target reconstruction of the rough shape of the head. Our method is to gradually "grow" the head mesh starting from the frontal face and extending to the rest of views using photometric stereo constraints. We call our method boundary-value growing algorithm. Results on photos of celebrities downloaded from the Internet are presented.

Connected through hyperedge HE-1 (induced by source paper "towards a complete 3d morphable model of the human head"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "towards high fidelity nonlinear 3d face morphable model"
    - "3d morphable face models past present and future"
    - "3dfacegan adversarial nets for 3d face representation generation and translation"
    - "combining 3d morphable models a large scale face and head model"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "deep pictorial gaze estimation"

Connected through hyperedge HE-2 (induced by source paper "combining 3d morphable models a large scale face and head model"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "3d face morphable models in the wild"
    - "morphable face models an open framework"
    - "review of statistical shape spaces for 3d data with comparative analysis for human faces"

Connected through hyperedge HE-3 (induced by source paper "fml face model learning from videos"):
  with members:
    - "on learning 3d face morphable model from in the wild images"
    - "learning category specific mesh reconstruction from image collections"
    - "statistical transformer networks learning shape and appearance models via self supervision"
    - "voxceleb2 deep speaker recognition"
    - "nonlinear 3d face morphable model"
    - "building statistical shape spaces for 3d human modeling"
    - "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 "video to fully automatic 3d hair model"):
  with members:
    - "3d face hallucination from a single depth frame"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "photorealistic facial texture inference using deep neural networks"
    - "learning detailed face reconstruction from a single image"
    - "3d face reconstruction by learning from synthetic data"
    - "conditional random fields as recurrent neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "reconstruction based disentanglement for pose invariant face recognition"):
  with members:
    - "towards large pose face frontalization in the wild"
    - "do we really need to collect millions of faces for effective face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "triplet probabilistic embedding for face verification and clustering"
    - "deep convolutional inverse graphics network"
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"
    - "effective face frontalization in unconstrained images"

Connected through hyperedge HE-6 (induced by source paper "reconstruction for feature disentanglement in pose invariant face recognition"):
  with members:
    - "towards large pose face frontalization in the wild"
    - "do we really need to collect millions of faces for effective face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "deep convolutional inverse graphics network"
    - "facenet a unified embedding for face recognition and clustering"
    - "effective face frontalization in unconstrained images"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"

Connected through hyperedge HE-7 (induced by source paper "regressing robust and discriminative 3d morphable models with a very deep neural network"):
  with members:
    - "do we really need to collect millions of faces for effective face recognition"
    - "disturblabel regularizing cnn on the loss layer"
    - "fitting a 3d morphable model to edges a comparison between hard and soft correspondences"
    - "3d face reconstruction by learning from synthetic data"
    - "deep residual learning for image recognition"
    - "effective face frontalization in unconstrained images"
    - "learning face representation from scratch"

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)