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: "rate splitting for multi antenna downlink precoder design and practical implementation"
  Abstract: Rate splitting (RS) is a potentially powerful and flexible technique for multi-antenna downlink transmission. In this paper, we address several technical challenges towards its practical implementation for beyond 5G systems. To this end, we focus on a single-cell system with a multi-antenna base station (BS) and K single-antenna receivers. We consider RS in its most general form, and joint decoding to fully exploit the potential of RS. First, we investigate the achievable rates under joint decoding and formulate the precoder design problems to maximize a general utility function, or to minimize the transmit power under pre-defined rate targets. Building upon the concave-convex procedure (CCCP), we propose precoder design algorithms for an arbitrary number of users. Our proposed algorithms approximate the intractable non-convex problems with a number of successively refined convex problems, and provably converge to stationary points of the original problems. Then, to reduce the decoding complexity, we consider the optimization of the precoder and the decoding order under successive decoding. Further, we propose a stream selection algorithm to reduce the number of precoded signals. With a reduced number of streams and successive decoding at the receivers, our proposed algorithm can even be implemented when the number of users is relatively large, whereas the complexity was previously considered as prohibitively high in the same setting. Finally, we propose a simple adaptation of our algorithms to account for the imperfection of the channel state information at the transmitter. Numerical results demonstrate that the general RS scheme provides a substantial performance gain as compared to state-of-the-art linear precoding schemes, especially with a moderately large number of users.

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)