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 "semidefinite relaxation and approximation analysis of a beamformed alamouti scheme for relay beamforming networks"):
  Abstract: In this paper, we study the amplify-and-forward (AF) schemes in two-hop one-way relay networks. In particular, we consider the multigroup multicast transmission between long-distance users. Given that perfect channel state information is perceived, our goal is to design the AF process so that the max-min-fair (MMF) signal-to-interference-plus-noise ratio (SINR) is optimized subject to generalized power constraints. We propose a rank-two beamformed Alamouti (BFA) AF scheme and formulate the corresponding AF design problem as a \emph{two-variable} fractional quadratically-constrained quadratic program (QCQP), which is further tackled by the semidefinite relaxation (SDR) technique. We analyze the approximation quality of two-variable fractional SDRs under the Gaussian randomization algorithm. These results are fundamentally new and reveal that the proposed BFA AF scheme can outperform the traditional BF AF scheme, especially when there are many users in the system or many generalized power constraints in the problem formulation. From a practical perspective, the BFA AF scheme offers two degrees of freedom (DoFs) in beamformer design, as opposed to the one DoF offered by the BF AF scheme, to improve the receivers' SINR. In the latter part of this paper, we demonstrate how this extra DoF leads to provable performance gains by considering two special cases of multicasting, where the AF process is shown to employ a special structure. The numerical simulations further validate that the proposed BFA AF scheme outperforms the BF AF scheme and works well for large-scale relay systems.

Includes members:
  Member "some proof derivations and further simulation results for semidefinite relaxation and approximation analysis of a beamformed alamouti scheme for relay beamforming networks":
    also connected through other hyperedges:
      (none)
  Member "rank two beamforming and power allocation in multicasting relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reel bf design achieving the sdp bound for downlink beamforming with arbitrary shaping constraints")
  Member "multicast multigroup beamforming for per antenna power constrained large scale arrays":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizations with intelligent reflecting surfaces irss in 6g wireless networks power control quality of service max min fair beamforming for unicast broadcast and multicast with multi antenna mobile users and multiple irss")
  Member "a stochastic beamformed amplify and forward scheme in a multigroup multicast mimo relay network with per antenna power constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "symbol level and multicast precoding for multiuser multiantenna downlink a survey classification and challenges")
  Member "physical layer multicasting by stochastic transmit beamforming and alamouti space time coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust transmissions in wireless powered multi relay networks with chance interference constraints")
  Member "group sparse beamforming for green cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse optimization for green edge ai inference")
  Member "mimo broadcasting for simultaneous wireless information and power transfer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "optimal spectrum sharing in mimo cognitive radio networks via semidefinite programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algorithm hardware co optimization of the memristor based framework for solving socp and homogeneous qcqp problems")

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)