human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a robust design for miso physical layer multicasting over line of sight channels"):
  Abstract: This letter studies a robust design problem for far-field line-of-sight (LOS) propagation channels where phase errors are present. Compared with the commonly used additive error model, the phase error model is more suitable for capturing the uncertainty in an LOS propagation channel, as the dominant source of uncertainty lies in the phase. We consider a multiple-input single-output multicast scenario, in which our goal is to design a beamformer that minimizes the transmit power while satisfying probabilistic signal-to-noise ratio constraints. In particular, the probabilistic constraints give rise to a new computational challenge, as they involve random trigonometric forms. In this study, we propose to first approximate the random trigonometric form by its second-order Taylor expansion and then tackle the resulting random quadratic form using a Bernstein-type inequality. It follows that an approximately optimal beamformer can be obtained using the standard semidefinite relaxation technique. Such a design approach is applicable to both independent and correlated phase errors. In the simulations, we first show that if a nonrobust design (i.e., one that does not take phase errors into account) is used; then, the whole system may collapse. We then show that our proposed method is less conservative than the existing robust design based on Gaussian approximation and thus requires a lower power budget.

Includes members:
  Member "physical layer multicasting by stochastic transmit beamforming and alamouti space time coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust transmissions in wireless powered multi relay networks with chance interference constraints")
  Member "generic optimization of linear precoding in multibeam satellite systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")
  Member "outage constrained robust transmit optimization for multiuser miso downlinks tractable approximations by conic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure millimeter wave cloud radio access networks relying on microwave multicast fronthaul")

Question: Please tell me which class the source paper 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)