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: "physical layer secret key generation in static environments"
  Abstract: We consider the problem of symmetric secret key generation in quasi-static/static environments, where the amount of common randomness obtained from the characteristics of the physical layer channel is limited. In order to provide a high-rate secret key generation mechanism, we introduce a method called induced randomness. In this method, locally generated randomness is assumed at the legitimate parties. Such randomness together with the uniqueness provided by the wireless channel coefficients are utilized to enable high-rate secret key generation. We describe the proposed secret key generation protocol for two scenarios; for the case where the legitimate parties have a direct link (the first scenario), and for the case where the legitimate parties do not have a direct link and communicate through an untrusted relay (the second scenario). After the exchange of the induced randomness, highly correlated samples are generated by the legitimate parties. These samples are then quantized, reconciled, and hashed to compensate for the information leakage to the eavesdropper and to allow verification of consistency of the generated key bit sequences. We utilize semantic security measures and information-theoretic inequalities to upper bound the probability of successful eavesdropping attack in terms of the mutual information measures that can be numerically computed. Given certain reasonable system parameters this bound is numerically evaluated to be $2^{-31}$ and $2^{-11.3}$ in the first and the second scenario, respectively. Furthermore, in the considered numerical setup, the bit generation rate is 64 bits/packet, the BER is 0.002% and 0.0029% in the first and the second scenario, respectively, and the eavesdropper's average BER in both scenarios is almost 50%. The probability of accepting a mismatched key is upper bounded and evaluated in the numerical setup to be 0.0015% for both scenarios.

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.IT(Information Theory)