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: "key management and learning based two level data security for metering infrastructure of smart grid"
  Abstract: In the smart grid, smart meters, and numerous control and monitoring applications employ bidirectional wireless communication, where security is a critical issue. In key management based encryption method for the smart grid, the Trusted Third Party (TTP), and links between the smart meter and the third party are assumed to be fully trusted and reliable. However, in wired/wireless medium, a man-in-middle may want to interfere, monitor and control the network, thus exposing its vulnerability. Acknowledging this, in this paper, we propose a novel two level encryption method based on two partially trusted simple servers (constitutes the TTP) which implement this method without increasing packet overhead. One server is responsible for data encryption between the meter and control center/central database, and the other server manages the random sequence of data transmission. Numerical calculation shows that the number of iterations required to decode a message is large which is quite impractical. Furthermore, we introduce One-class support vector machine (machine learning) algorithm for node-to-node authentication utilizing the location information and the data transmission history (node identity, packet size and frequency of transmission). This secures data communication privacy without increasing the complexity of the conventional key management scheme.

Connected through hyperedge HE-1 (induced by source paper "key management systems for smart grid advanced metering infrastructure a survey"):
  with members:
    - "smart grid communications overview of research challenges solutions and standardization activities"
    - "minimizing electricity theft using smart meters in ami"

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.MA(Multiagent Systems)