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 "driver gaze estimation in the real world overcoming the eyeglass challenge"):
  Abstract: A driver's gaze is critical for determining the driver's attention level, state, situational awareness, and readiness to take over control from partially and fully automated vehicles. Tracking both the head and eyes (pupils) can provide reliable estimation of a driver's gaze using face images under ideal conditions. However, the vehicular environment introduces a variety of challenges that are usually unaccounted for - harsh illumination, nighttime conditions, and reflective/dark eyeglasses. Unfortunately, relying on head pose alone under such conditions can prove to be unreliable owing to significant eye movements. In this study, we offer solutions to address these problems encountered in the real world. To solve issues with lighting, we demonstrate that using an infrared camera with suitable equalization and normalization usually suffices. To handle eyeglasses and their corresponding artifacts, we adopt the idea of image-to-image translation using generative adversarial networks (GANs) to pre-process images prior to gaze estimation. To this end, we propose the Gaze Preserving CycleGAN (GPCycleGAN). As the name suggests, this network preserves the driver's gaze while removing potential eyeglasses from infrared face images. GPCycleGAN is based on the well-known CycleGAN approach, with the addition of a gaze classifier and a gaze consistency loss for additional supervision. Our approach exhibits improved performance and robustness on challenging real-world data spanning 13 subjects and a variety of driving conditions.

Includes members:
  Member "travelgan image to image translation by transformation vector learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reusing discriminators for encoding towards unsupervised image to image translation")
  Member "unsupervised eyeglasses removal in the wild":
    also connected through other hyperedges:
      (none)
  Member "openpose realtime multi person 2d pose estimation using part affinity fields":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "multimodal unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "a style based generator architecture for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "driver gaze zone estimation using convolutional neural networks a general framework and ablative analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dada a large scale benchmark and model for driver attention prediction in accidental scenarios")
  Member "a review and analysis of eye gaze estimation systems algorithms and performance evaluation methods in consumer platforms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "are you really looking at me a feature extraction framework for estimating interpersonal eye gaze from conventional video")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)