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: "non orthogonal unicast and broadcast transmission via joint beamforming and ldm in cellular networks"
  Abstract: Limited bandwidth resources and higher energy efficiency requirements motivate incorporating multicast and broadcast transmission into the next-generation cellular network architectures, particularly for multimedia streaming applications. Layered division multiplexing (LDM), a form of NOMA, can potentially improve unicast throughput and broadcast coverage with respect to traditional orthogonal frequency division multiplexing (FDM) or time division multiplexing (TDM), by simultaneously using the same frequency and time resources for multiple unicast or broadcast transmissions. In this paper, the performance of LDM-based unicast and broadcast transmission in a cellular network is studied by assuming a single frequency network (SFN) operation for the broadcast layer, while allowing arbitrarily clustered cooperation among the base stations (BSs) for the transmission of unicast data streams. Beamforming and power allocation between unicast and broadcast layers, the so-called injection level in the LDM literature, are optimized with the aim of minimizing the sum-power under constraints on the user-specific unicast rates and on the common broadcast rate. The effects of imperfect channel coding and imperfect CSI are also studied to gain insights into robust implementation in practical systems. The non-convex optimization problem is tackled by means of successive convex approximation (SCA) techniques. Performance upper bounds are also presented by means of the $\rm{S}$-procedure followed by semidefinite relaxation (SDR). Finally, a dual decomposition-based solution is proposed to facilitate an efficient distributed implementation of LDM where the optimal unicast beamforming vectors can be obtained locally by the cooperating BSs. Numerical results are presented, which show the tightness of the proposed bounds and hence the near-optimality of the proposed solutions.

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)