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: "facial landmark detection a literature survey"
  Abstract: The locations of the fiducial facial landmark points around facial components and facial contour capture the rigid and non-rigid facial deformations due to head movements and facial expressions. They are hence important for various facial analysis tasks. Many facial landmark detection algorithms have been developed to automatically detect those key points over the years, and in this paper, we perform an extensive review of them. We classify the facial landmark detection algorithms into three major categories: holistic methods, Constrained Local Model (CLM) methods, and the regression-based methods. They differ in the ways to utilize the facial appearance and shape information. The holistic methods explicitly build models to represent the global facial appearance and shape information. The CLMs explicitly leverage the global shape model but build the local appearance models. The regression based methods implicitly capture facial shape and appearance information. For algorithms within each category, we discuss their underlying theories as well as their differences. We also compare their performances on both controlled and in the wild benchmark datasets, under varying facial expressions, head poses, and occlusion. Based on the evaluations, we point out their respective strengths and weaknesses. There is also a separate section to review the latest deep learning based algorithms. The survey also includes a listing of the benchmark databases and existing software. Finally, we identify future research directions, including combining methods in different categories to leverage their respective strengths to solve landmark detection "in-the-wild".

Connected through hyperedge HE-1 (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"
    - "decafa deep convolutional cascade for face alignment in the wild"
    - "look at boundary a boundary aware face alignment algorithm"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "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-2 (induced by source paper "kneel knee anatomical landmark localization using hourglass networks"):
  with members:
    - "geometric image correspondence verification by dense pixel matching"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "dgc net dense geometric correspondence network"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "a general and adaptive robust loss function"
    - "mixup beyond empirical risk minimization"
    - "automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks"
    - "a novel method for automatic localization of joint area on knee plain radiographs"

Connected through hyperedge HE-3 (induced by source paper "multiple face analyses through adversarial learning"):
  with members:
    - "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"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "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-4 (induced by source paper "real time ultrasound enhanced multimodal imaging of tongue using 3d printable stabilizer system a deep learning approach"):
  with members:
    - "transfer learning for ultrasound tongue contour extraction with different domains"
    - "object detection with deep learning a review"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "simultaneous regression and feature learning for facial landmarking"):
  with members:
    - "joint face alignment and 3d face reconstruction with application to face recognition"
    - "a functional regression approach to facial landmark tracking"
    - "supervised descent method for solving nonlinear least squares problems in computer vision"

Connected through hyperedge HE-6 (induced by source paper "neural sign language translation based on human keypoint estimation"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "context encoding for semantic segmentation"
    - "attention is all you need"
    - "don t decay the learning rate increase the batch size"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "squeeze and excitation networks"
    - "contrastive learning for image captioning"
    - "hand keypoint detection in single images using multiview bootstrapping"

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)