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: "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.

Connected through hyperedge HE-1 (induced by source paper "scaling pseudonymous authentication for large mobile systems"):
  with members:
    - "efficient scalable and resilient vehicle centric certificate revocation list distribution in vanets"
    - "blockpki an automated resilient and transparent public key infrastructure"
    - "a security credential management system for v2x communications"
    - "vpkiaas a highly available and dynamically scalable vehicular public key infrastructure"
    - "rhythm a randomized hybrid scheme to hide in the mobile crowd"
    - "secmace scalable and robust identity and credential management infrastructure in vehicular communication systems"
    - "evaluating on demand pseudonym acquisition policies in vehicular communication systems"
    - "the key to intelligent transportation identity and credential management in vehicular communication systems"

Connected through hyperedge HE-2 (induced by source paper "secmace scalable and robust identity and credential management infrastructure in vehicular communication systems"):
  with members:
    - "resilient privacy protection for location based services through decentralization"
    - "evaluating on demand pseudonym acquisition policies in vehicular communication systems"
    - "the key to intelligent transportation identity and credential management in vehicular communication systems"
    - "towards deploying a scalable robust vehicular identity and credential management infrastructure"
    - "vespa vehicular security and privacy preserving architecture"
    - "on the road reflections on the security of vehicular communication systems"
    - "secure vehicular communication systems implementation performance and research challenges"
    - "impact of vehicular communications security on transportation safety"

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)