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: "fixed region beamforming using frequency diverse subarray for secure mmwave wireless communications"
  Abstract: Millimeter-wave (mmWave) using conventional phased array (CPA) enables highly directional and fixed angular beamforming (FAB), therefore enhancing physical layer security (PLS) in the angular domain. However, as the eavesdropper is located in the direction pointed by the mainlobe of the information-carrying beam, information leakage is inevitable and FAB cannot guarantee PLS performance. To address this threat, we propose a novel fixed region beamforming (FRB) by employing a frequency diverse subarray (FDSA) architecture to enhance the PLS performance for mmWave directional communications. In particular, we carefully introduce multiple frequency offset increments (FOIs) across subarrays to achieve a sophisticated beampattern synthesis that ensures a confined information transmission only within the desired angle-range region (DARR) in close vicinity of the target user. More specifically, we formulate the secrecy rate maximization problem with FRB over possible subarray FOIs, and consider two cases of interests, i.e., without/with the location information of eavesdropping, both turn out to be NP-hard. For the unknown eavesdropping location case, we propose a seeker optimization algorithm to minimize the maximum sidelobe peak of the beampattern outside the DARR. As for the known eavesdropping location case, a block coordinate descend linear approximation algorithm is proposed to minimize the sidelobe level in the eavesdropping region. By using the proposed FRB, the mainlobes of all subarrays are constructively superimposed in the DARR while the sidelobes are destructively overlayed outside the DARR. Therefore, FRB takes prominent effect on confining information transmission within the DARR. Numerical simulations demonstrate that the proposed FDSA-based FRB can provide superior PLS performance over the CPA-based FAB.

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)