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: "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"
  Abstract: This paper describes our submission to the 1st 3D Face Alignment in the Wild (3DFAW) Challenge. Our method builds upon the idea of convolutional part heatmap regression (Bulat and Tzimiropoulos, 2016), extending it for 3D face alignment. Our method decomposes the problem into two parts: (a) X,Y (2D) estimation and (b) Z (depth) estimation. At the first stage, our method estimates the X,Y coordinates of the facial landmarks by producing a set of 2D heatmaps, one for each landmark, using convolutional part heatmap regression. Then, these heatmaps, alongside the input RGB image, are used as input to a very deep subnetwork trained via residual learning for regressing the Z coordinate. Our method ranked 1st in the 3DFAW Challenge, surpassing the second best result by more than 22 %. Code can be found at http://www.cs.nott.ac.uk/~psxab5/.

Connected through hyperedge HE-1 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "3fabrec fast few shot face alignment by reconstruction"):
  with members:
    - "teacher supervises students how to learn from partially labeled images for facial landmark detection"
    - "few shot adversarial learning of realistic neural talking head models"
    - "aggregation via separation boosting facial landmark detector with semi supervised style translation"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "unsupervised learning of landmarks by descriptor vector exchange"
    - "adaptive wing loss for robust face alignment via heatmap regression"
    - "decafa deep convolutional cascade for face alignment in the wild"
    - "look at boundary a boundary aware face alignment algorithm"

Connected through hyperedge HE-4 (induced by source paper "joint 3d face reconstruction and dense alignment with position map regression network"):
  with members:
    - "multi attribute robust component analysis for facial uv maps"
    - "side information for face completion a robust pca approach"
    - "densereg fully convolutional dense shape regression in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "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"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-5 (induced by source paper "facial landmark point localization using coarse to fine deep recurrent neural network"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "improving landmark localization with semi supervised learning"
    - "adversarial learning of structure aware fully convolutional networks for landmark localization"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "recurrent human pose estimation"
    - "robust face alignment using a mixture of invariant experts"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "face alignment using k cluster regression forests with weighted splitting"

Connected through hyperedge HE-6 (induced by source paper "densereg fully convolutional dense shape regression in the wild"):
  with members:
    - "unite the people closing the loop between 3d and 2d human representations"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "human pose estimation via convolutional part heatmap regression"
    - "supervised transformer network for efficient face detection"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "dense human body correspondences using convolutional networks"
    - "spatial transformer networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "joint voxel and coordinate regression for accurate 3d facial landmark localization"):
  with members:
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "integral human pose regression"
    - "multi context attention for human pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "human pose estimation via convolutional part heatmap regression"

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)