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 "joint action unit localisation and intensity estimation through heatmap regression"):
  Abstract: This paper proposes a supervised learning approach to jointly perform facial Action Unit (AU) localisation and intensity estimation. Contrary to previous works that try to learn an unsupervised representation of the Action Unit regions, we propose to directly and jointly estimate all AU intensities through heatmap regression, along with the location in the face where they cause visible changes. Our approach aims to learn a pixel-wise regression function returning a score per AU, which indicates an AU intensity at a given spatial location. Heatmap regression then generates an image, or channel, per AU, in which each pixel indicates the corresponding AU intensity. To generate the ground-truth heatmaps for a target AU, the facial landmarks are first estimated, and a 2D Gaussian is drawn around the points where the AU is known to cause changes. The amplitude and size of the Gaussian is determined by the intensity of the AU. We show that using a single Hourglass network suffices to attain new state of the art results, demonstrating the effectiveness of such a simple approach. The use of heatmap regression allows learning of a shared representation between AUs without the need to rely on latent representations, as these are implicitly learned from the data. We validate the proposed approach on the BP4D dataset, showing a modest improvement on recent, complex, techniques, as well as robustness against misalignment errors. Code for testing and models will be available to download from this https URL

Includes members:
  Member "a functional regression approach to facial landmark tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inferring dynamic representations of facial actions from a still image")
  Member "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "first order motion model for image animation")
  Member "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "cascaded continuous regression for real time incremental face tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards omni supervised face alignment for large scale unlabeled videos")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")

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)