human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms"
  Abstract: In this paper, a review is presented for the research on eye gaze estimation techniques and applications, which has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive, and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for the performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.

Connected through hyperedge HE-1 (induced by source paper "driver gaze estimation in the real world overcoming the eyeglass challenge"):
  with members:
    - "travelgan image to image translation by transformation vector learning"
    - "unsupervised eyeglasses removal in the wild"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "driver gaze zone estimation using convolutional neural networks a general framework and ablative analysis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised cross domain image generation"

Connected through hyperedge HE-2 (induced by source paper "are you really looking at me a feature extraction framework for estimating interpersonal eye gaze from conventional video"):
  with members:
    - "real time eye gaze direction classification using convolutional neural network"
    - "appearance based gaze estimation in the wild"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-3 (induced by source paper "are you really looking at me a framework for extracting interpersonal eye gaze from conventional video"):
  with members:
    - "automated dyadic data recorder addr framework and analysis of facial cues in deceptive communication"
    - "appearance based gaze estimation in the wild"
    - "rendering of eyes for eye shape registration and gaze estimation"
    - "pupil an open source platform for pervasive eye tracking and mobile gaze based interaction"

Connected through hyperedge HE-4 (induced by source paper "unclogging our arteries using human inspired signals to disambiguate navigational intentions"):
  with members:
    - "understanding teacher gaze patterns for robot learning"

Connected through hyperedge HE-5 (induced by source paper "efficient cnn implementation for eye gaze estimation on low power low quality consumer imaging systems"):
  with members:
    - "smart augmentation learning an optimal data augmentation strategy"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"
    - "fast and accurate algorithm for eye localisation for gaze tracking in low resolution images"
    - "it s written all over your face full face appearance based gaze estimation"
    - "appearance based gaze estimation in the wild"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "recurrent cnn for 3d gaze estimation using appearance and shape cues"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "stacked hourglass networks for human pose estimation"
    - "eye tracking for everyone"
    - "it s written all over your face full face appearance based gaze estimation"
    - "appearance based gaze estimation in the wild"
    - "rendering of eyes for eye shape registration and gaze estimation"

Connected through hyperedge HE-7 (induced by source paper "subjective annotations for vision based attention level estimation"):
  with members:
    - "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"
    - "poseidon face from depth for driver pose estimation"

Question: Please tell me which class the center node 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.HC(Human-Computer Interaction)