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: "nonreciprocity compensation combined with turbo codes for secret key generation in vehicular ad hoc social iot networks"
  Abstract: The physical attributes of the dynamic vehicle-to-vehicle propagation channel can be utilized for the generation of highly random and symmetric cryptographic keys. However, in a physical-layer key agreement scheme, nonreciprocity due to inherent channel noise and hardware impairments can propagate bit disagreements. This has to be addressed prior to the symmetric key generation which is inherently important in Social Internet of Things networks, including in adversarial settings (e.g., battlefields). In this paper, we parametrically incorporate temporal variability attributes, such as 3-D scattering and scatterers’ mobility. Accordingly, this is the first work to incorporate such features into the key generation process by combining nonreciprocity compensation with turbo codes (TCs). Preliminary results indicate a significant improvement when using TCs in bit mismatch rate and key generation rate in comparison to sample indexing techniques.

Connected through hyperedge HE-1 (induced by source paper "design and implementation of secret key agreement for platoon based vehicular cyber physical systems"):
  with members:
    - "h2b heartbeat based secret key generation using piezo vibration sensors"
    - "lcd low latency command dissemination for a platoon of vehicles"
    - "exploiting channel diversity in secret key generation from multipath fading randomness"
    - "cooperative secret key generation from phase estimation in narrowband fading channels"

Connected through hyperedge HE-2 (induced by source paper "empirical vulnerability analysis of automated smart contracts security testing on blockchains"):
  with members:
    - "tool demonstration fsolidm for designing secure ethereum smart contracts"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "internet of things security and forensics challenges and opportunities"
    - "smart contract based access control for the internet of things"
    - "designing secure ethereum smart contracts a finite state machine based approach"
    - "statistical model checking an overview"

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)