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 "learning to personalize in appearance based gaze tracking"):
  Abstract: In this paper, we investigate how to efficiently combine appearance-based methods and personal calibration for video-based gaze estimation. Previous calibration methods have learned an average model for all persons and then adapted it for a specific person. In contrast, we directly learn a model for personalized gaze estimation where the personal variations are modeled as a low-dimensional latent parameter space. During training, the network learns an efficient way to leverage these parameters for estimating gaze. After training, little person-specific data is required to optimize these (few) parameters. In fact, by calibrating only six parameters per person, the accuracy can be improved by a factor of up to 2.5, and reaches the same level as person-specific models. #R##N#We assess the performance of our method on a large dataset, consisting of high resolution images taken from near-infrared cameras with active illumination. As it turns out, our method is the first appearance-based approach to gaze estimation to reach the same level of accuracy as involved model-based methods. We further demonstrate that it outperforms existing approaches on two datasets with relatively low-resolution images, MPIIGaze and GazeCapture. Finally, we show using a synthetic dataset created using UnityEyes tools, that our method efficiently predicts 3D gaze (origin and direction of gaze) without the need of explicitly 3D-annotated training data.

Includes members:
  Member "few shot adaptive gaze estimation":
    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 "learning to find eye region landmarks for remote gaze estimation in unconstrained settings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint iris segmentation and localization using deep multi task learning framework")
  Member "mpiigaze real world dataset and deep appearance based gaze estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an efficient method of training small models for regression problems with knowledge distillation")
  Member "eye tracking for everyone":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")
  Member "it s written all over your face full face appearance based gaze estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)