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: "unified performance analysis of orthogonal transmit beamforming methods with user selection"
  Abstract: Simultaneous multiuser beamforming in multiantenna downlink channels can entail dirty paper (DP) precoding (optimal and high complexity) or linear precoding (suboptimal and low complexity) approaches. The system performance is typically characterized by the sum capacity with homogenous users with perfect channel state information at the transmitter. The sum capacity performance analysis requires the exact probability distributions of the user signal-to-noise ratios (SNRs) or signal-to-interference plus noise ratios (SINRs). The standard techniques from order statistics can be sufficient to obtain the probability distributions of SNRs for DP precoding due to the removal of known interference at the transmitter. Derivation of such probability distributions for linear precoding techniques on the other hand is much more challenging. For example, orthogonal beamforming techniques do not completely cancel the interference at the user locations, thereby requiring the analysis with SINRs. In this paper, we derive the joint probability distributions of the user SINRs for two orthogonal beamforming methods combined with user scheduling: adaptive orthogonal beamforming and orthogonal linear beamforming. We obtain compact and unified solutions for the joint probability distributions of the scheduled users' SINRs. Our analytical results can be applied for similar algorithms and are verified by computer simulations.

Connected through hyperedge HE-1 (induced by source paper "exact joint distribution analysis of zero forcing v blast gains with greedy ordering"):
  with members:
    - "performance analysis of optimum zero forcing beamforming with greedy user selection"
    - "on outage and error rate analysis of the ordered v blast"

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)