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: "balance queueing and retransmission latency optimal massive mimo design"
  Abstract: One fundamental challenge in 5G URLLC is how to optimize massive MIMO communication systems for achieving both low latency and high reliability. A reasonable design is to choose the smallest possible target error rate, which can achieve the highest link reliability and the minimum retransmission latency. However, the overall system latency is the sum of latency due to queueing and due to retransmissions, hence choosing the smallest target error rate does not always minimize the overall latency. In this paper, we minimize the overall latency by jointly designing the target error rate and transmission rate, which leads to a sweet tradeoff point between queueing latency and retransmission latency. This design problem can be formulated as a Markov decision process, whose complexity is prohibitively high for real-system deployments. Nonetheless, we managed to develop a low-complexity closed-form policy named Large-arraY Reliability and Rate Control (LYRRC), which is latency-optimal in the large-array regime. In LYRRC, the target error rate is a function of the antenna number, arrival rate, and channel estimation error; and the optimal transmission rate is twice of the arrival rate. Using over-the-air channel measurements, our evaluations suggest that LYRRC can satisfy both the latency and reliability requirements of 5G URLLC.

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)