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: "dense face alignment"
  Abstract: Face alignment is a classic problem in the computer vision field. Previous works mostly focus on sparse alignment with a limited number of facial landmark points, i.e., facial landmark detection. In this paper, for the first time, we aim at providing a very dense 3D alignment for large-pose face images. To achieve this, we train a CNN to estimate the 3D face shape, which not only aligns limited facial landmarks but also fits face contours and SIFT feature points. Moreover, we also address the bottleneck of training CNN with multiple datasets, due to different landmark markups on different datasets, such as 5, 34, 68. Experimental results show our method not only provides high-quality, dense 3D face fitting but also outperforms the state-of-the-art facial landmark detection methods on the challenging datasets. Our model can run at real time during testing.

Connected through hyperedge HE-1 (induced by source paper "face detection with feature pyramids and landmarks"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "robust and high performance face detector"
    - "self driving cars a survey"
    - "refineface refinement neural network for high performance face detection"
    - "searching for mobilenetv3"
    - "pyramidbox high performance detector for finding tiny face"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "retinaface single stage dense face localisation in the wild"

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 "lprnet lightweight deep network by low rank pointwise residual convolution"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "mnasnet platform aware neural architecture search for mobile"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (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-5 (induced by source paper "3d dense face alignment via graph convolution networks"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "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"
    - "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"
    - "geometric deep learning going beyond euclidean data"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-6 (induced by source paper "real time memory efficient large pose face alignment via deep evolutionary network"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "style aggregated network for facial landmark detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "validation loss for landmark detection"):
  with members:
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "style aggregated network for facial landmark detection"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "sphereface deep hypersphere embedding for face recognition"
    - "end to end 3d face reconstruction with deep neural networks"
    - "pose invariant face alignment with a single cnn"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "joint 3d face reconstruction and dense face alignment from a single image with 2d assisted self supervised learning"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "nonlinear 3d face morphable model"
    - "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"

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)