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 "appearance based gaze estimation via gaze decomposition and single gaze point calibration"):
  Abstract: Appearance-based gaze estimation provides relatively unconstrained gaze tracking. However, subject-indepen\-dent models achieve limited accuracy partly due to individual variations. To improve estimation, we propose a novel gaze decomposition method and a single gaze point calibration method, motivated by our finding that the inter-subject squared bias exceeds the intra-subject variance for a subject-independent estimator. We decompose the gaze angle into a subject-dependent bias term and a subject-independent difference term between the gaze angle and the bias. The difference term is estimated by a deep convolutional network. For calibration-free tracking, we set the subject-dependent bias term to zero. For single gaze point calibration, we estimate the bias from a few images taken as the subject gazes at a point. Experiments on three datasets indicate that as a calibration-free estimator, the proposed method outperforms the state-of-the-art methods that use single model by up to $10.0\%$. The proposed calibration method is robust and reduces estimation error significantly (up to $35.6\%$), achieving state-of-the-art performance for appearance-based eye trackers with calibration.

Includes members:
  Member "using variable dwell time to accelerate gaze based web browsing with two step selection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "appearance based gaze estimation using dilated convolutions")
  Member "appearance based gaze estimation using dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")
  Member "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")
  Member "light weight head pose invariant gaze tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "magiceyes a large scale eye gaze estimation dataset for mixed reality")
  Member "batch renormalization towards reducing minibatch dependence in batch normalized models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rethinking batch normalization in transformers")
  Member "mpiigaze real world dataset and deep appearance based gaze estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an efficient method of training small models for regression problems with knowledge distillation")

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)