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: "robust face alignment using a mixture of invariant experts"
  Abstract: Face alignment, which is the task of finding the locations of a set of facial landmark points in an image of a face, is useful in widespread application areas. Face alignment is particularly challenging when there are large variations in pose (in-plane and out-of-plane rotations) and facial expression. To address this issue, we propose a cascade in which each stage consists of a mixture of regression experts. Each expert learns a customized regression model that is specialized to a different subset of the joint space of pose and expressions. The system is invariant to a predefined class of transformations (e.g., affine), because the input is transformed to match each expert’s prototype shape before the regression is applied. We also present a method to include deformation constraints within the discriminative alignment framework, which makes our algorithm more robust. Our algorithm significantly outperforms previous methods on publicly available face alignment datasets.

Connected through hyperedge HE-1 (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"
    - "pose invariant face alignment with a single cnn"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "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-2 (induced by source paper "aggregation via separation boosting facial landmark detector with semi supervised style translation"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "taskonomy disentangling task transfer learning"
    - "multimodal unsupervised image to image translation"
    - "style aggregated network for facial landmark detection"

Connected through hyperedge HE-3 (induced by source paper "verification of very low resolution faces using an identity preserving deep face super resolution network"):
  with members:
    - "attention aware face hallucination via deep reinforcement learning"
    - "level playing field for million scale face recognition"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep cascaded bi network for face hallucination"
    - "accurate image super resolution using very deep convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "deep imbalanced attribute classification using visual attention aggregation"):
  with members:
    - "deep visual attention prediction"
    - "harmonious attention network for person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "focal loss for dense object detection"
    - "curriculum learning for multi task classification of visual attributes"
    - "order free rnn with visual attention for multi label classification"

Connected through hyperedge HE-5 (induced by source paper "facial landmark machines a backbone branches architecture with progressive representation learning"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "quantized densely connected u nets for efficient landmark localization"
    - "contrast oriented deep neural networks for salient object detection"
    - "unsupervised discovery of object landmarks as structural representations"
    - "style aggregated network for facial landmark detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "attentive crowd flow machines"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"

Connected through hyperedge HE-6 (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"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"
    - "face alignment using k cluster regression forests with weighted splitting"

Connected through hyperedge HE-7 (induced by source paper "interspecies knowledge transfer for facial keypoint detection"):
  with members:
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "do we really need to collect millions of faces for effective face recognition"
    - "what makes imagenet good for transfer learning"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "end to end localization and ranking for relative attributes"

Connected through hyperedge HE-8 (induced by source paper "self reinforced cascaded regression for face alignment"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning from simulated and unsupervised images through adversarial training"
    - "selecting relevant web trained concepts for automated event retrieval"

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)