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: "secrecy energy efficient robust beamforming in wireless networks of information and energy transfer"
  Abstract: Next-generation communication networks are positively looking towards the energy-efficient transfer of information and energy over the same wireless channel. Thus, users would be able to receive information and harvest energy. However, under the requirement of smaller base station (BS)-to-users (UEs) distances and higher received power levels, the physical layer will become more vulnerable to cyber attacks by potential multi-antenna eavesdroppers. To realize secure wireless information and power transfer, we consider transmit time-switching (TS) mode, where energy and information signal are transmitted separately in time by the BS. Such protocol is not only quite easier to implement than the receive power splitting (PS) approach but also delivers the opportunity of multi-purpose beamforming, where energy beamformers during wireless power transfer are useful in jamming the eavesdropper. In the presence of imperfect channel estimation and multi-antenna eavesdroppers, the energy and information beamformers and the transmit TS ratio are jointly optimized to maximize the worst-case user secrecy rate subject to UEs' harvested energy thresholds and BS transmit power budget. This secrecy rate optimization problem is very complicated due many computationally challenging constraints. A new robust path-following algorithm for its computational solution is proposed, which involves one simple convex quadratic program (QP) at each iteration. Robust secrecy energy efficiency maximization, which is further complex due to additional function of optimization variables in the denominator of secrecy rate function is also addressed. Numerical results confirm that performance of the proposed computational solutions is robust against the channel uncertainties.

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)