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 survey of augmented reality navigation"
  Abstract: Navigation has been a popular area of research in both academia and industry. Combined with maps, and different localization technologies, navigation systems have become robust and more usable. By combining navigation with augmented reality, it can be improved further to become realistic and user friendly. This paper surveys existing researches carried out in this area, describes existing techniques for building augmented reality navigation systems, and the problems faced.

Connected through hyperedge HE-1 (induced by source paper "deep neural network and data augmentation methodology for off axis iris segmentation in wearable headsets"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "segdensenet iris segmentation for pre and post cataract surgery"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gesture based continuous authentication for wearable devices the google glass case"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "dels 3d deep localization and segmentation with a 3d semantic map"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "icnet for real time semantic segmentation on high resolution images"
    - "semantic video cnns through representation warping"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "demon depth and motion network for learning monocular stereo"
    - "long short term memory kalman filters recurrent neural estimators for pose regularization"
    - "playing for benchmarks"

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)