human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "facial landmark correlation analysis"):
  Abstract: We present a facial landmark position correlation analysis as well as its applications. Although numerous facial landmark detection methods have been presented in the literature, few of them concern the intrinsic relationship among the landmarks. In order to reveal and interpret this relationship, we propose to analyze the facial landmark correlation by using Canonical Correlation Analysis (CCA). We experimentally show that dense facial landmark annotations in current benchmarks are strongly correlated, and we propose several applications based on this analysis. First, we give insights into the predictions from different facial landmark detection models (including cascaded random forests, cascaded Convolutional Neural Networks (CNNs), heatmap regression models) and interpret how CNNs progressively learn to predict facial landmarks. Second, we propose a few-shot learning method that allows to considerably reduce manual effort for dense landmark annotation. To this end, we select a portion of landmarks from the dense annotation format to form a sparse format, which is mostly correlated to the rest of them. Thanks to the strong correlation among the landmarks, the entire set of dense facial landmarks can then be inferred from the annotation in the sparse format by transfer learning. Unlike the previous methods, we mainly focus on how to find the most efficient sparse format to annotate. Overall, our correlation analysis provides new perspectives for the research on facial landmark detection.

Includes members:
  Member "similarity of neural network representations revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explaining knowledge distillation by quantifying the knowledge")
  Member "teacher supervises students how to learn from partially labeled images for facial landmark detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pixel in pixel net towards efficient facial landmark detection in the wild")
  Member "facial landmark detection a literature survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kneel knee anatomical landmark localization using hourglass networks")
  Member "decafa deep convolutional cascade for face alignment in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3fabrec fast few shot face alignment by reconstruction")
  Member "look at boundary a boundary aware face alignment algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "kpnet towards minimal face detector")
  Member "towards highly accurate and stable face alignment for high resolution videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anti confusing region aware network for human pose estimation")
  Member "deep multi center learning for face alignment":
    also connected through other hyperedges:
      (none)
  Member "wing loss for robust facial landmark localisation with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "kpnet towards minimal face detector")

Question: Please tell me which class the source paper 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)