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: "extreme 3d face reconstruction looking past occlusions"
  Abstract: Existing single view, 3D face reconstruction methods can produce beautifully detailed 3D results, but typically only for near frontal, unobstructed viewpoints. We describe a system designed to provide detailed 3D reconstructions of faces viewed under extreme conditions, out of plane rotations, and occlusions. Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e.g., wrinkles). We estimate a coarse 3D face shape which acts as a foundation and then separately layer this foundation with details represented by a bump map. We show how a deep convolutional encoder-decoder can be used to estimate such bump maps. We further show how this approach naturally extends to generate plausible details for occluded facial regions. We test our approach and its components extensively, quantitatively demonstrating the invariance of our estimated facial details. We further provide numerous qualitative examples showing that our method produces detailed 3D face shapes in viewing conditions where existing state of the art often break down.

Connected through hyperedge HE-1 (induced by source paper "transferability and hardness of supervised classification tasks"):
  with members:
    - "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"
    - "beyond sharing weights for deep domain adaptation"
    - "task2vec task embedding for meta learning"
    - "on measuring the iconicity of a face"
    - "the information complexity of learning tasks their structure and their distance"
    - "toward understanding catastrophic forgetting in continual learning"
    - "regularized learning for domain adaptation under label shifts"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-2 (induced by source paper "on learning 3d face morphable model from in the wild images"):
  with members:
    - "representation learning by rotating your faces"
    - "face alignment in full pose range a 3d total solution"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "unsupervised training for 3d morphable model regression"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "learning dense facial correspondences in unconstrained images"

Connected through hyperedge HE-3 (induced by source paper "fsgan subject agnostic face swapping and reenactment"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "cr gan learning complete representations for multi view generation"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "triple consistency loss for pairing distributions in gan based face synthesis"
    - "ganimation anatomically aware facial animation from a single image"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "fsnet an identity aware generative model for image based face swapping"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (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"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"

Connected through hyperedge HE-5 (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 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"
    - "generative and discriminative voxel modeling with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "expnet landmark free deep 3d facial expressions"):
  with members:
    - "faceposenet making a case for landmark free face alignment"
    - "do we really need to collect millions of faces for effective face recognition"
    - "a multi scale cascade fully convolutional network face detector"
    - "pooling faces template based face recognition with pooled face images"
    - "fitting a 3d morphable model to edges a comparison between hard and soft correspondences"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning detailed face reconstruction from a single image"
    - "convolutional experts constrained local model for facial landmark detection"

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)