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: "pose invariant face alignment with a single cnn"
  Abstract: Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on the cascade of regressors, e.g., CNN, which has shown promising results. Nonetheless, the cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end training, hand-crafted features and slow training speed. To address these issues, we propose a new layer, named visualization layer, that can be integrated into the CNN architecture and enables joint optimization with different loss functions. Extensive evaluation of the proposed method on multiple datasets demonstrates state-of-the-art accuracy, while reducing the training time by more than half compared to the typical cascade of CNNs. In addition, we compare multiple CNN architectures with the visualization layer to further demonstrate the advantage of its utilization.

Connected through hyperedge HE-1 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces"):
  with members:
    - "spatially transformed adversarial examples"
    - "fast geometrically perturbed adversarial faces"
    - "style aggregated network for facial landmark detection"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "more is less a more complicated network with less inference complexity"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition"):
  with members:
    - "representation learning by rotating your faces"
    - "densereg fully convolutional dense shape regression in the wild"
    - "nonlinear 3d face morphable model"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "robust face alignment using a mixture of invariant experts"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "representation learning by rotating your faces"):
  with members:
    - "on learning 3d face morphable model from in the wild images"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "additive margin softmax for face verification"
    - "nonlinear 3d face morphable model"
    - "began boundary equilibrium generative adversarial networks"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "towards large pose face frontalization in the wild"
    - "do we really need to collect millions of faces for effective face recognition"

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 "sfa small faces attention face detector"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "detecting faces using region based fully convolutional networks"
    - "face detection using deep learning an improved faster rcnn approach"
    - "recurrent scale approximation for object detection in cnn"
    - "ssh single stage headless face detector"
    - "face r cnn"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "faceboxes a cpu real time face detector with high accuracy"

Connected through hyperedge HE-6 (induced by source paper "teacher supervises students how to learn from partially labeled images for facial landmark detection"):
  with members:
    - "network pruning via transformable architecture search"
    - "few example object detection with model communication"
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "quantized densely connected u nets for efficient landmark localization"
    - "autoloss learning discrete schedules for alternate optimization"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "exploring disentangled feature representation beyond face identification"
    - "style aggregated network for facial landmark detection"

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"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "dense face alignment"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"

Connected through hyperedge HE-8 (induced by source paper "towards omni supervised face alignment for large scale unlabeled videos"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "look at boundary a boundary aware face alignment algorithm"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "relation networks for object detection"
    - "self adversarial training for human pose estimation"
    - "data distillation towards omni supervised learning"

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)