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: "towards highly accurate and stable face alignment for high resolution videos"
  Abstract: In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation. However, Conventional Heatmap Regression (CHR) is not accurate nor stable when dealing with high-resolution facial videos, since it finds the maximum activated location in heatmaps which are generated from rounding coordinates, and thus leads to quantization errors when scaling back to the original high-resolution space. In this paper, we propose a Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment. The proposed FHR can accurately estimate the fractional part according to the 2D Gaussian function by sampling three points in heatmaps. To further stabilize the landmarks among continuous video frames while maintaining the precise at the same time, we propose a novel stabilization loss that contains two terms to address time delay and non-smooth issues, respectively. Experiments on 300W, 300-VW and Talking Face datasets clearly demonstrate that the proposed method is more accurate and stable than the state-of-the-art models.

Connected through hyperedge HE-1 (induced by source paper "anti confusing region aware network for human pose estimation"):
  with members:
    - "human pose estimation with spatial contextual information"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "devil in the details towards accurate single and multiple human parsing"
    - "multi scale structure aware network for human pose estimation"
    - "learning to refine human pose estimation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"

Connected through hyperedge HE-2 (induced by source paper "facial landmark correlation analysis"):
  with members:
    - "similarity of neural network representations revisited"
    - "teacher supervises students how to learn from partially labeled images for facial landmark detection"
    - "facial landmark detection a literature survey"
    - "decafa deep convolutional cascade for face alignment in the wild"
    - "look at boundary a boundary aware face alignment algorithm"
    - "deep multi center learning for face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-3 (induced by source paper "on learning disentangled representations for gait recognition"):
  with members:
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "towards high fidelity nonlinear 3d face morphable model"
    - "gait recognition via disentangled representation learning"
    - "retinaface single stage dense face localisation in the wild"
    - "synthesizing images of humans in unseen poses"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-4 (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"
    - "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"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-5 (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"
    - "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"
    - "pose invariant face alignment with a single cnn"
    - "self adversarial training for human pose estimation"
    - "data distillation towards omni supervised learning"

Connected through hyperedge HE-6 (induced by source paper "what is the relationship between face alignment and facial expression recognition"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "advanced local motion patterns for macro and micro facial expression recognition"
    - "red net a recurrent encoder decoder network for video based face alignment"
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "style aggregated network for facial landmark detection"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-7 (induced by source paper "dsfd dual shot face detector"):
  with members:
    - "face detection using improved faster rcnn"
    - "arcface additive angular margin loss for deep face recognition"
    - "pyramidbox a context assisted single shot face detector"
    - "detnet a backbone network for object detection"
    - "selective refinement network for high performance face detection"
    - "focal loss for dense object detection"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "face attention network an effective face detector for the occluded faces"

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)