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 the capacity and energy efficiency of training based transmissions over fading channels"):
  Abstract: In this paper, the capacity and energy efficiency of training-based communication schemes employed for transmission over a-priori unknown Rayleigh block fading channels are studied. In these schemes, periodically transmitted training symbols are used at the receiver to obtain the minimum mean-square-error (MMSE) estimate of the channel fading coefficients. Initially, the case in which the product of the estimate error and transmitted signal is assumed to be Gaussian noise is considered. In this case, it is shown that bit energy requirements grow without bound as the signal-to-noise ratio (SNR) goes to zero, and the minimum bit energy is achieved at a nonzero SNR value below which one should not operate. The effect of the block length on both the minimum bit energy and the SNR value at which the minimum is achieved is investigated. Flash training and transmission schemes are analyzed and shown to improve the energy efficiency in the low-SNR regime. #R##N#In the second part of the paper, the capacity and energy efficiency of training-based schemes are investigated when the channel input is subject to peak power constraints. The capacity-achieving input structure is characterized and the magnitude distribution of the optimal input is shown to be discrete with a finite number of mass points. The capacity, bit energy requirements, and optimal resource allocation strategies are obtained through numerical analysis. The bit energy is again shown to grow without bound as SNR decreases to zero due to the presence of peakedness constraints. The improvements in energy efficiency when on-off keying with fixed peak power and vanishing duty cycle is employed are studied. Comparisons of the performances of training-based and noncoherent transmission schemes are provided.

Includes members:
  Member "pilot symbol assisted communications with noncausal and causal wiener filters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ergodic capacity analysis in cognitive radio systems under channel uncertainty")
  Member "training optimization for gauss markov rayleigh fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal training for non feedback adaptive psam over time varying rayleigh fading channels")
  Member "capacity of sparse multipath channels in the ultra wideband regime":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity scaling in a non coherent wideband massive simo block fading channel")
  Member "the noncoherent rician fading channel part i structure of the capacity achieving input":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a simple capacity lower bound for communication with superimposed pilots")
  Member "noncoherent rician fading channel part ii spectral efficiency in the low power regime":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the miso channel with feedback can infinitely massive antennas achieve infinite capacity")
  Member "channel uncertainty in ultra wideband communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral efficiency of random time hopping cdma")
  Member "capacity per unit energy of fading channels with a peak constraint":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unified capacity limit of non coherent wideband fading channels")

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)