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: "gesture based continuous authentication for wearable devices the google glass case"
  Abstract: We study the feasibility of touch gesture behavioural biometrics for implicit authentication of users on a smartglass (Google Glass) by proposing a continuous authentication system using two classifiers: SVM with RBF kernel, and a new classifier based on Chebyshev's concentration inequality. Based on data collected from 30 volunteers, we show that such authentication is feasible both in terms of classification accuracy and computational load on smartglasses. We achieve a classification accuracy of up to 99% with only 75 training samples using behavioural biometric data from four different types of touch gestures. To show that our system can be generalized, we test its performance on touch data from smartphones and found the accuracy to be similar to smartglasses. Finally, our experiments on the permanence of gestures show that the negative impact of changing user behaviour with time on classification accuracy can be best alleviated by periodically replacing older training samples with new randomly chosen samples.

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"
    - "a survey of augmented reality navigation"
    - "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"
    - "imagenet large scale visual recognition challenge"

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.CR(Cryptography and Security)