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 "mpiigaze real world dataset and deep appearance based gaze estimation"):
  Abstract: Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected under laboratory conditions and methods were not evaluated across multiple datasets. Our work makes three contributions towards addressing these limitations. First, we present the MPIIGaze that contains 213,659 full face images and corresponding ground-truth gaze positions collected from 15 users during everyday laptop use over several months. An experience sampling approach ensured continuous gaze and head poses and realistic variation in eye appearance and illumination. To facilitate cross-dataset evaluations, 37,667 images were manually annotated with eye corners, mouth corners, and pupil centres. Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze. We study key challenges including target gaze range, illumination conditions, and facial appearance variation. We show that image resolution and the use of both eyes affect gaze estimation performance while head pose and pupil centre information are less informative. Finally, we propose GazeNet, the first deep appearance-based gaze estimation method. GazeNet improves the state of the art by 22% percent (from a mean error of 13.9 degrees to 10.8 degrees) for the most challenging cross-dataset evaluation.

Includes members:
  Member "eye tracking for everyone":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")
  Member "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "magiceyes a large scale eye gaze estimation dataset for mixed reality")
  Member "it s written all over your face full face appearance based gaze estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")
  Member "appearance based gaze estimation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geddnet a network for gaze estimation with dilation and decomposition")
  Member "rendering of eyes for eye shape registration and gaze estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "magiceyes a large scale eye gaze estimation dataset for mixed reality")
  Member "turkergaze crowdsourcing saliency with webcam based eye tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "contextual encoder decoder network for visual saliency prediction")
  Member "prediction of search targets from fixations in open world settings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a self validation network for object level human attention estimation")
  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)