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: "convolutional experts constrained local model for facial landmark detection"
  Abstract: Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regression-based approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories. In our work, we present a novel local detector -- Convolutional Experts Network (CEN) -- that brings together the advantages of neural architectures and mixtures of experts in an end-to-end framework. We further propose a Convolutional Experts Constrained Local Model (CE-CLM) algorithm that uses CEN as local detectors. We demonstrate that our proposed CE-CLM algorithm outperforms competitive state-of-the-art baselines for facial landmark detection by a large margin on four publicly-available datasets. Our approach is especially accurate and robust on challenging profile images.

Connected through hyperedge HE-1 (induced by source paper "deep learning for face recognition a critical analysis"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "face attention network an effective face detector for the occluded faces"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "the do s and don ts for cnn based face verification"

Connected through hyperedge HE-2 (induced by source paper "facial expression recognition using human to animated character expression translation"):
  with members:
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "facial expression recognition using disentangled adversarial learning"
    - "deep facial expression recognition a survey"
    - "island loss for learning discriminative features in facial expression recognition"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "peak piloted deep network for facial expression recognition"
    - "context encoders feature learning by inpainting"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "facial expression recognition using disentangled adversarial learning"):
  with members:
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "deep facial expression recognition a survey"
    - "island loss for learning discriminative features in facial expression recognition"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "peak piloted deep network for facial expression recognition"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "understanding beauty via deep facial features"):
  with members:
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "a light cnn for deep face representation with noisy labels"
    - "going deeper with convolutions"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-5 (induced by source paper "procrustes registration of two dimensional statistical shape models without correspondences"):
  with members:
    - "model order selection in statistical shape models"
    - "a weighting strategy for active shape models"
    - "point set registration coherent point drift"

Connected through hyperedge HE-6 (induced by source paper "deep energies for estimating three dimensional facial pose and expression"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "generative face completion"
    - "deep image prior"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "combining data driven and model driven methods for robust facial landmark detection"):
  with members:
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "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"
    - "dense face alignment"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "multimodal sentiment analysis with word level fusion and reinforcement learning"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "recent trends in deep learning based natural language processing"
    - "image captioning with semantic attention"
    - "select additive learning improving cross individual generalization in multimodal sentiment analysis"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "neural architecture search with reinforcement learning"
    - "select additive learning improving generalization in multimodal sentiment analysis"

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)