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 "geddnet a network for gaze estimation with dilation and decomposition"):
  Abstract: Appearance-based gaze estimation from RGB images provides relatively unconstrained gaze tracking from commonly available hardware. The accuracy of subject-independent models is limited partly by small intra-subject and large inter-subject variations in appearance, and partly by a latent subject-dependent bias. To improve estimation accuracy, we propose to use dilated-convolutions in a deep convolutional neural network to capture subtle changes in the eye images, and a novel gaze decomposition method that decomposes the gaze angle into the sum of a subject-independent gaze estimate from the image and a subject-dependent bias. To further reduce estimation error, we propose a calibration method that estimates the bias from a few images taken as the subject gazes at only a few or even just a single gaze target. This significantly redues calibration time and complexity. Experiments on four datasets, including a new dataset we collected containing large variations in head pose and face location, indicate that even without calibration the estimator already outperforms state-of-the-art methods by more than 6.3%. The proposed calibration method is robust to the location of calibration target and reduces estimation error significantly (up to 35.6%), achieving state-of-the-art performance with much less calibration data than required by previously proposed methods.

Includes members:
  Member "offset calibration for appearance based gaze estimation via gaze decomposition":
    also connected through other hyperedges:
      (none)
  Member "photo realistic monocular gaze redirection using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "content consistent generation of realistic eyes with style")
  Member "appearance based gaze estimation using dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "offset calibration for appearance based gaze estimation via gaze decomposition")
  Member "few shot adaptive gaze estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a coarse to fine adaptive network for appearance based gaze estimation")
  Member "improving few shot user specific gaze adaptation via gaze redirection synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a generalized and robust method towards practical gaze estimation on smart phone")
  Member "a differential approach for gaze estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised representation learning for gaze estimation")
  Member "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention flow end to end joint attention estimation")
  Member "recurrent cnn for 3d gaze estimation using appearance and shape cues":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving few shot user specific gaze adaptation via gaze redirection synthesis")

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)