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: "space time block diagonalization for frequency selective mimo broadcast channels"
  Abstract: The most relevant linear precoding method for frequency-flat MIMO broadcast channels is block diagonalization (BD) which, under certain conditions, attains the same nonlinear dirty paper coding channel capacity. However, BD is not easily translated to frequency-selective channels, since space-time information is required for transceiver design. In this paper, we demonstrate that BD is feasible in frequency-selective MIMO broadcast channels to eliminate inter-user interference (IUI) if the transmit block length is sufficiently large, and if the number of transmit antennas is greater than the number of users. We also propose three different approaches to mitigate/eliminate inter-symbol interference (ISI) in block transmissions: i) time-reversal-based BD (TRBD) which maximizes spatial focusing around the receivers using transmitter processing only, ii) equalized BD (EBD) which minimizes the ISI using transmitter processing only, and iii) joint processing BD (JPBD), which uses linear processing at the transmitter and the receiver to suppress ISI. We analyze the theoretical diversity and multiplexing gains of these techniques, and we demonstrate that JPBD approximates full multiplexing gain for a sufficiently large transmit block length. Extensive numerical simulations show that the achievable rate and probability of error performance of all the proposed methods improve those of conventional time-reversal beamforming. Moreover, JPBD provides the highest achievable rate region for frequency-selective MIMO broadcast channels.

Connected through hyperedge HE-1 (induced by source paper "downlink power optimization for heterogeneous networks with time reversal based transmission under backhaul limitation"):
  with members:
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "5g cellular key enabling technologies and research challenges"
    - "uplink performance of time reversal mrc in massive mimo systems subject to phase noise"
    - "time reversal based transmissions with distributed power allocation for two tier networks"
    - "power control in two tier femtocell 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)