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 "on an achievable rate of large rayleigh block fading mimo channels with no csi"):
  Abstract: Training-based transmission over Rayleigh block-fading multiple-input multiple-output (MIMO) channels is investigated. As a training method a combination of a pilot-assisted scheme and a biased signaling scheme is considered. The achievable rates of successive decoding (SD) receivers based on the linear minimum mean-squared error (LMMSE) channel estimation are analyzed in the large-system limit, by using the replica method under the assumption of replica symmetry. It is shown that negligible pilot information is best in terms of the achievable rates of the SD receivers in the large-system limit. The obtained analytical formulas of the achievable rates can improve the existing lower bound on the capacity of the MIMO channel with no channel state information (CSI), derived by Hassibi and Hochwald, for all SNRs. The comparison between the obtained bound and a high-SNR approximation of the channel capacity, derived by Zheng and Tse, implies that the high-SNR approximation is unreliable unless quite high SNR is considered. Energy efficiency in the low-SNR regime is also investigated in terms of the power per information bit required for reliable communication. The required minimum power is shown to be achieved at a positive rate for the SD receiver with no CSI, whereas it is achieved in the zero-rate limit for the case of perfect CSI available at the receiver. Moreover, numerical simulations imply that the presented large-system analysis can provide a good approximation for not so large systems. The results in this paper imply that SD schemes can provide a significant performance gain in the low-to-moderate SNR regimes, compared to conventional receivers based on one-shot channel estimation.

Includes members:
  Member "large system analysis of joint channel and data estimation for mimo ds cdma systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymptotic optimality of massive mimo systems using densely spaced transmit antennas")
  Member "on the capacity of large mimo block fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi user detection based on expectation propagation for the non coherent simo multiple access channel")
  Member "optimization of sequences in cdma systems a statistical mechanics approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing")
  Member "microscopic analysis for decoupling principle of linear vector channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "large system analysis of joint channel and data estimation for mimo ds cdma systems")
  Member "asymptotic analysis of general multiuser detectors in mimo ds cdma channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymptotic performance analysis of a k hop amplify and forward relay mimo channel")
  Member "on the capacity and energy efficiency of training based transmissions over fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pilot based channel estimation design in covert wireless communication")
  Member "analysis of cdma systems that are characterized by eigenvalue spectrum":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spectral method for phase retrieval an expectation propagation perspective")
  Member "randomly spread cdma asymptotics via statistical physics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")

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)