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: "adaptive hybrid beamforming with massive phased arrays in macro cellular networks"
  Abstract: Hybrid beamforming via large antenna arrays has shown a great potential in increasing data rate for next generation of cellular networks. It has been shown that several data streams can be delivered simultaneously in such hybrid systems. In this paper, different algorithms are proposed for designing beamforming vectors in such systems. It is assumed that the macro base station is equipped with a massive phased array incapable of adjusting the amplitude gain and phase shift of the antennas at each time slot due to hardware limitations. The non-uniform distribution of users is exploited to generate multiple static beams to serve crowded areas such as shopping malls and office buildings. The problem is formulated as a network utility maximization. First, the problem is studied when the base station has a single RF chain (single beam scenario). Semi-definite relaxation (SDR) with randomization is used to solve the problem. As a second approach, a low-complexity heuristic beam composition algorithm is proposed which performs very close to the upper-bound obtained by SDR. Next, the problem is studied for a generic number of RF chains (multi-beam scenario). An iterative algorithm called gradient projection is used to numerically obtain locally optimal beamforming vectors in the multi-beam scenario. Numerical results reveal that using optimized beamforming vectors produced by the proposed algorithms can lead to close to 5x network throughput improvement over conventional LTE networks.

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)