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: "privacy leakage in mobile computing tools methods and characteristics"
  Abstract: The number of smartphones, tablets, sensors, and connected wearable devices are rapidly increasing. Today, in many parts of the globe, the penetration of mobile computers has overtaken the number of traditional personal computers. This trend and the always-on nature of these devices have resulted in increasing concerns over the intrusive nature of these devices and the privacy risks that they impose on users or those associated with them. In this paper, we survey the current state of the art on mobile computing research, focusing on privacy risks and data leakage effects. We then discuss a number of methods, recommendations, and ongoing research in limiting the privacy leakages and associated risks by mobile computing.

Connected through hyperedge HE-1 (induced by source paper "privacy in trajectory micro data publishing a survey"):
  with members:
    - "towards matching user mobility traces in large scale datasets"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "where you are is who you are user identification by matching statistics"
    - "spatio temporal techniques for user identification by means of gps mobility data"
    - "tracking human mobility using wifi signals"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "protecting locations with differential privacy under temporal correlations"

Connected through hyperedge HE-2 (induced by source paper "evaluating the information security awareness of smartphone users"):
  with members:
    - "android permissions remystified a field study on contextual integrity"

Connected through hyperedge HE-3 (induced by source paper "security privacy and safety risk assessment for virtual reality learning environment applications"):
  with members:
    - "the effect of iot new features on security and privacy new threats existing solutions and challenges yet to be solved"
    - "towards a social virtual reality learning environment in high fidelity"
    - "advanced cloud privacy threat modeling"

Connected through hyperedge HE-4 (induced by source paper "a hybrid deep learning architecture for privacy preserving mobile analytics"):
  with members:
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "federated learning strategies for improving communication efficiency"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "protecting sensory data against sensitive inferences"
    - "deep learning in mobile and wireless networking a survey"
    - "deepsecure scalable provably secure deep learning"
    - "towards bottom up analysis of social food"
    - "inverting visual representations with convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving deep inference for rich user data on the cloud"):
  with members:
    - "deepsecure scalable provably secure deep learning"
    - "towards bottom up analysis of social food"
    - "going deeper in facial expression recognition using deep neural networks"
    - "inverting visual representations with convolutional networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "caffe convolutional architecture for fast feature embedding"

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)