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 "security and privacy in vehicular social networks"):
  Abstract: Over the last two decades, social networking over the Internet has attracted the attention of millions of users and becomes a new factor that drives business intelligence and economic growth worldwide. People have strong social instincts and they tend to socialise and communicate with each other in every possible scenario thus, social networks have been extended to different types and forms beyond the Internet. Mobile Social Networks (MSN) represent a new form of social networks that take advantage of the close proximity and the unique features offered by mobile devices to establish social links among mobile users. More recently, Vehicular Social Networks (VSN), a special type of MSNs, have emerged as a new communication paradigm for social networking on the roads. VSNs are decentralised opportunistic communication networks formed among vehicles (Vegni and Loscri 2015), where the communication takes place in three dimensions: human to human, human to machine, and machine to machine communications. The flourish of vehicular networks development over the last decade has made the social interaction on the roads possible promising more enjoyable experience for vehicular travellers.#R##N#Prior to discussing VSNs and their connectivity patterns, it is important to develop an understating of vehicular networks on one hand and the human social behaviour in VSNs from a social theory viewpoint on the other hand. Vehicular networks are characterised with high mobility and frequent network topology changes, which make the connectivity between vehicles tends to be vulnerable. On the other side, in addition to being within the communication range of each other, sharing the same social interests and/or characteristics is a prerequisite for any two vehicles to socially interact in VSNs.#R##N#This chapter is intended to present a novel social connectivity model for VSNs by utilising the evolving graph theory. First, the properties of VSNs are briefly introduced in the context of vehicular networks. The social metrics of the communicating vehicles are then reviewed using the concepts of social theory along with the conventional connectivity metrics in vehicular networks.#R##N#Thereafter, a novel social evolving graph-based connectivity model that considers both social and conventional metrics of the communicating vehicles is developed using an extended version of the evolving graph. Moreover, the proposed connectivity model suggests new social links with vehicles that enter the communication area of other vehicles with similar social interests. Finally, the developed connectivity model is investigated in a highway scenario to demonstrate its abilities#R##N#in capturing the evolving characteristics of social interactions among vehicles and selecting the best paths to forward data. Data forwarding decisions are made based on a combination of social and communication metrics of the communicating vehicles. Simulation results showed that the proposed connectivity model facilitates the social interactions among vehicles and is able to establish reliable social paths among the communicating vehicles.

Includes members:
  Member "resilient collaborative privacy for location based services":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resilient privacy protection for location based services through decentralization")
  Member "the key to intelligent transportation identity and credential management in vehicular communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from data to actions in intelligent transportation systems a prescription of functional requirements for model actionability")
  Member "towards deploying a scalable robust vehicular identity and credential management infrastructure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resilient collaborative privacy for location based services")
  Member "vespa vehicular security and privacy preserving architecture":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "resilient privacy protection for location based services through decentralization")
  Member "secure vehicular communication systems design and architecture":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling pseudonymous authentication for large mobile systems")

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.CR(Cryptography and Security)