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: "a stochastic beamformed amplify and forward scheme in a multigroup multicast mimo relay network with per antenna power constraints"
  Abstract: In this paper, we consider a two-hop one-way relay network for multigroup multicast transmission between long-distance users, in which the relay is equipped with multiple antennas, while the transmitters and receivers are all with a single antenna. Assuming that perfect channel state information is available, we study amplify-and-forward (AF) schemes that aim at optimizing the max-min-fair (MMF) rate. We begin by considering the classic beamformed AF (BF-AF) scheme, whose corresponding MMF design problem can be formulated as a rank-constrained fractional semidefinite program (SDP). We show that the gap between the BF-AF rate and the SDR rate associated with an optimal SDP solution is sensitive to the number of users as well as the number of power constraints in the relay system. This reveals that the BF-AF scheme may not be well suited for large-scale systems. We therefore propose the stochastic beamformed AF (SBF-AF) schemes, which differ from the BF-AF scheme in that time-varying AF weights are used. We prove that the MMF rates of the proposed SBF-AF schemes are at most $0.8317$ bits/s/Hz less than the SDR rate, irrespective of the number of users or power constraints. Thus, SBF-AF can outperform BF-AF especially in large-scale systems. Finally, we present numerical results to demonstrate the viability of our proposed schemes.

Connected through hyperedge HE-1 (induced by source paper "symbol level and multicast precoding for multiuser multiantenna downlink a survey classification and challenges"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "an overview on resource allocation techniques for multi user mimo systems"
    - "spatio temporal waveform design for multiuser massive mimo downlink with 1 bit receivers"
    - "joint beamforming and broadcasting in massive mimo"
    - "coordinate update algorithms for robust power loading for the mu miso downlink with outage constraints"
    - "energy efficient symbol level precoding in multiuser miso based on relaxed detection region"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "adaptive pilot clustering in heterogeneous massive mimo networks"

Connected through hyperedge HE-2 (induced by source paper "unraveling the rank one solution mystery of robust miso downlink transmit optimization a verifiable sufficient condition via a new duality result"):
  with members:
    - "coordinate update algorithms for robust power loading for the mu miso downlink with outage constraints"
    - "a parallel decomposition method for nonconvex stochastic multi agent optimization problems"
    - "optimal multiuser transmit beamforming a difficult problem with a simple solution structure"
    - "physical layer multicasting by stochastic transmit beamforming and alamouti space time coding"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"
    - "worst case robust multiuser transmit beamforming using semidefinite relaxation duality and implications"
    - "robust linear precoder design for multi cell downlink transmission"
    - "outage constrained robust transmit optimization for multiuser miso downlinks tractable approximations by conic optimization"

Connected through hyperedge HE-3 (induced by source paper "semidefinite relaxation and approximation analysis of a beamformed alamouti scheme for relay beamforming networks"):
  with members:
    - "some proof derivations and further simulation results for semidefinite relaxation and approximation analysis of a beamformed alamouti scheme for relay beamforming networks"
    - "rank two beamforming and power allocation in multicasting relay networks"
    - "multicast multigroup beamforming for per antenna power constrained large scale arrays"
    - "physical layer multicasting by stochastic transmit beamforming and alamouti space time coding"
    - "group sparse beamforming for green cloud ran"
    - "mimo broadcasting for simultaneous wireless information and power transfer"
    - "optimal spectrum sharing in mimo cognitive radio networks via semidefinite programming"

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)