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: "iterative deterministic equivalents for the performance analysis of communication systems"
  Abstract: In this article, we introduce iterative deterministic equivalents as a novel technique for the performance analysis of communication systems whose channels are modeled by complex combinations of independent random matrices. This technique extends the deterministic equivalent approach for the study of functionals of large random matrices to a broader class of random matrix models which naturally arise as channel models in wireless communications. We present two specific applications: First, we consider a multi-hop amplify-and-forward (AF) MIMO relay channel with noise at each stage and derive deterministic approximations of the mutual information after the Kth hop. Second, we study a MIMO multiple access channel (MAC) where the channel between each transmitter and the receiver is represented by the double-scattering channel model. We provide deterministic approximations of the mutual information, the signal-to-interference-plus-noise ratio (SINR) and sum-rate with minimum-mean-square-error (MMSE) detection and derive the asymptotically optimal precoding matrices. In both scenarios, the approximations can be computed by simple and provably converging fixed-point algorithms and are shown to be almost surely tight in the limit when the number of antennas at each node grows infinitely large. Simulations suggest that the approximations are accurate for realistic system dimensions. The technique of iterative deterministic equivalents can be easily extended to other channel models of interest and is, therefore, also a new contribution to the field of random matrix theory.

Connected through hyperedge HE-1 (induced by source paper "asymptotic analysis of rzf over double scattering channels with mmse estimation"):
  with members:
    - "massive mimo has unlimited capacity"
    - "multi cell massive mimo performance with double scattering channels"
    - "no downlink pilots are needed in tdd massive mimo"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "random beamforming over quasi static and fading channels a deterministic equivalent approach"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

Connected through hyperedge HE-2 (induced by source paper "large system analysis of cognitive radio network via partially projected regularized zero forcing precoding"):
  with members:
    - "super resolution sparse mimo ofdm channel estimation based on spatial and temporal correlations"
    - "joint beamforming and power control in coordinated multicell max min duality effective network and large system transition"
    - "multi pair amplify and forward relaying with very large antenna arrays"
    - "large system analysis of cooperative multi cell downlink transmission via regularized channel inversion with imperfect csit"
    - "optimal power allocation and user loading for multiuser miso channels with regularized channel inversion"
    - "on capacity of large scale mimo multiple access channels with distributed sets of correlated antennas"
    - "large system analysis of linear precoding in miso broadcast channels with confidential messages"
    - "message passing algorithm for distributed downlink regularized zero forcing beamforming with cooperative base stations"

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)