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: "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
  Abstract: Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision. The existing methods usually perform each task independently and sequentially, ignoring their interactions. To tackle this problem, we propose a unified framework for simultaneous facial landmark detection, head pose estimation, and facial deformation analysis, and the proposed model is robust to facial occlusion. Following a cascade procedure augmented with model-based head pose estimation, we iteratively update the facial landmark locations, facial occlusion, head pose and facial de- formation until convergence. The experimental results on benchmark databases demonstrate the effectiveness of the proposed method for simultaneous facial landmark detection, head pose and facial deformation estimation, even if the images are under facial occlusion.

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"
    - "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"
    - "more is less a more complicated network with less inference complexity"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "facial action unit detection using attention and relation learning"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"

Connected through hyperedge HE-3 (induced by source paper "mobilefan transferring deep hidden representation for face alignment"):
  with members:
    - "structured knowledge distillation for dense prediction"
    - "look at boundary a boundary aware face alignment algorithm"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "multi label learning based deep transfer neural network for facial attribute classification"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"
    - "improving fast segmentation with teacher student learning"

Connected through hyperedge HE-4 (induced by source paper "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "residual codean autoencoder for facial attribute analysis"
    - "single shot temporal action detection"
    - "single shot text detector with regional attention"
    - "scale aware face detection"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"

Connected through hyperedge HE-5 (induced by source paper "2d wasserstein loss for robust facial landmark detection"):
  with members:
    - "semantic alignment finding semantically consistent ground truth for facial landmark detection"
    - "high resolution representations for labeling pixels and regions"
    - "adaptive wing loss for robust face alignment via heatmap regression"
    - "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"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-6 (induced by source paper "tree gated deep mixture of experts for pose robust face alignment"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "tree gated deep regressor ensemble for face alignment in the wild"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "adaptive neural trees"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "fine grained head pose estimation without keypoints"
    - "pose invariant face alignment with a single cnn"
    - "improving landmark localization with semi supervised learning"

Connected through hyperedge HE-7 (induced by source paper "multiple face analyses through adversarial learning"):
  with members:
    - "facial landmark detection a literature survey"
    - "style aggregated network for facial landmark detection"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "improving landmark localization with semi supervised learning"
    - "adversarial multi task learning for text classification"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "face attribute prediction using off the shelf cnn features"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"

Connected through hyperedge HE-8 (induced by source paper "unsupervised discovery of object landmarks as structural representations"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning to generate long term future via hierarchical prediction"
    - "convolutional neural network architecture for geometric matching"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "unsupervised part learning for visual recognition"
    - "mask r cnn"
    - "unsupervised learning of object landmarks by factorized spatial embeddings"

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)