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 "improving few shot user specific gaze adaptation via gaze redirection synthesis"):
  Abstract: As an indicator of human attention gaze is a subtle behavioral cue which can be exploited in many applications. However, inferring 3D gaze direction is challenging even for deep neural networks given the lack of large amount of data (groundtruthing gaze is expensive and existing datasets use different setups) and the inherent presence of gaze biases due to person-specific difference. In this work, we address the problem of person-specific gaze model adaptation from only a few reference training samples. The main and novel idea is to improve gaze adaptation by generating additional training samples through the synthesis of gaze-redirected eye images from existing reference samples. In doing so, our contributions are threefold: (i) we design our gaze redirection framework from synthetic data, allowing us to benefit from aligned training sample pairs to predict accurate inverse mapping fields; (ii) we proposed a self-supervised approach for domain adaptation; (iii) we exploit the gaze redirection to improve the performance of person-specific gaze estimation. Extensive experiments on two public datasets demonstrate the validity of our gaze retargeting and gaze estimation framework.

Includes members:
  Member "recurrent cnn for 3d gaze estimation using appearance and shape cues":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")
  Member "deep pictorial gaze estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")
  Member "appearance based 3d gaze estimation with personal calibration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "magiceyes a large scale eye gaze estimation dataset for mixed reality")
  Member "ganimation anatomically aware facial animation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "mpiigaze real world dataset and deep appearance based gaze estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an efficient method of training small models for regression problems with knowledge distillation")
  Member "gazedirector fully articulated eye gaze redirection in video":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "photo realistic monocular gaze redirection using generative adversarial networks")
  Member "eye tracking for everyone":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")

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)