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: "adaptive wing loss for robust face alignment via heatmap regression"
  Abstract: Heatmap regression has became one of the mainstream approaches to localize facial landmarks. As Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are becoming popular in solving computer vision tasks, extensive research has been done on these architectures. However, the loss function for heatmap regression is rarely studied. In this paper, we analyze the ideal loss function properties for heatmap regression in face alignment problems. Then we propose a novel loss function, named Adaptive Wing loss, that is able to adapt its shape to different types of ground truth heatmap pixels. This adaptability decreases the loss to zero on foreground pixels while leaving some loss on background pixels. To address the imbalance between foreground and background pixels, we also propose Weighted Loss Map, which assigns high weights on foreground and difficult background pixels to help training process focus more on pixels that are crucial to landmark localization. To further improve face alignment accuracy, we introduce boundary prediction and CoordConv with boundary coordinates. Extensive experiments on different benchmarks, including COFW, 300W and WFLW, show our approach outperforms the state-of-the-art by a significant margin on various evaluation metrics. Besides, the Adaptive Wing loss also helps other heatmap regression tasks. Code will be made publicly available.

Connected through hyperedge HE-1 (induced by source paper "3fabrec fast few shot face alignment by reconstruction"):
  with members:
    - "teacher supervises students how to learn from partially labeled images for facial landmark detection"
    - "few shot adversarial learning of realistic neural talking head models"
    - "aggregation via separation boosting facial landmark detector with semi supervised style translation"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "unsupervised learning of landmarks by descriptor vector exchange"
    - "decafa deep convolutional cascade for face alignment in the wild"
    - "look at boundary a boundary aware face alignment algorithm"
    - "densereg fully convolutional dense shape regression in the wild"

Connected through hyperedge HE-2 (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"
    - "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"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

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)