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: "end to end 3d face reconstruction with deep neural networks"
  Abstract: Monocular 3D facial shape reconstruction from a single 2D facial image has been an active research area due to its wide applications. Inspired by the success of deep neural networks (DNN), we propose a DNN-based approach for End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different from recent works that reconstruct and refine the 3D face in an iterative manner using both an RGB image and an initial 3D facial shape rendering, our DNN model is end-to-end, and thus the complicated 3D rendering process can be avoided. Moreover, we integrate in the DNN architecture two components, namely a multi-task loss function and a fusion convolutional neural network (CNN) to improve facial expression reconstruction. With the multi-task loss function, 3D face reconstruction is divided into neutral 3D facial shape reconstruction and expressive 3D facial shape reconstruction. The neutral 3D facial shape is class-specific. Therefore, higher layer features are useful. In comparison, the expressive 3D facial shape favors lower or intermediate layer features. With the fusion-CNN, features from different intermediate layers are fused and transformed for predicting the 3D expressive facial shape. Through extensive experiments, we demonstrate the superiority of our end-to-end framework in improving the accuracy of 3D face reconstruction.

Connected through hyperedge HE-1 (induced by source paper "training efficient network architecture and weights via direct sparsity control"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "channel pruning for accelerating very deep neural networks"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"
    - "pruning filters for efficient convnets"
    - "dynamic network surgery for efficient dnns"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-2 (induced by source paper "dense 3d object reconstruction from a single depth view"):
  with members:
    - "defo net learning body deformation using generative adversarial networks"
    - "mcgan mean and covariance feature matching gan"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning a multi view stereo machine"

Connected through hyperedge HE-3 (induced by source paper "3dfacegan adversarial nets for 3d face representation generation and translation"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "unsupervised training for 3d morphable model regression"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "began boundary equilibrium generative adversarial networks"
    - "deriving neural architectures from sequence and graph kernels"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "face to parameter translation for game character auto creation"):
  with members:
    - "unsupervised training for 3d morphable model regression"
    - "arbitrary style transfer with deep feature reshuffle"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-5 (induced by source paper "adaptive wing loss for robust face alignment via heatmap regression"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-6 (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"
    - "learning dense facial correspondences in unconstrained images"
    - "extreme 3d face reconstruction looking past occlusions"

Connected through hyperedge HE-7 (induced by source paper "facsimile fast and accurate scans from an image in less than a second"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "deep ordinal regression network for monocular depth estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "siclope silhouette based clothed people"
    - "detailed human avatars from monocular video"

Connected through hyperedge HE-8 (induced by source paper "text based editing of talking head video"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "visemenet audio driven animator centric speech animation"
    - "unsupervised training for 3d morphable model regression"
    - "a hybrid model for identity obfuscation by face replacement"
    - "everybody dance now"

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)