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 "dispersion analysis of infinite constellations in ergodic fading channels"):
  Abstract: This thesis considers infinite constellations in fading channels, without power constraint and with perfect channel state information available at the receiver. Infinite constellations are the framework, proposed by Poltyrev, for analyzing coded modulation codes. The Poltyrev's capacity, is the highest achievable normalized log density (NLD) of codewords per unit volume, at possibly large block length, that guarantees a vanishing error probability. For a given finite block length and a fixed error probability, there is a gap between the highest achievable NLD and Poltyrev's capacity. The dispersion analysis quantifies asymptotically this gap. #R##N#The thesis begins by the dispersion analysis of infinite constellations in scalar fading channels. Later on, we extend the analysis to the case of multiple input multiple output fading channels. As in other channels, we show that the gap between the highest achievable NLD and the Poltyrev's capacity, vanishes asymptotically as the square root of the channel dispersion over the block length, multiplied by the inverse Q-function of the allowed error probability. #R##N#Moreover, exact terms for Poltyrev's capacity and channel dispersion, are derived in the thesis. The relations to the amplitude and to the power constrained fading channels are also discussed, especially in terms of capacity, channel dispersion and error exponents. These relations hint that in typical cases the unconstrained model can be interpreted as the limit of the constrained model, when the signal to noise ratio tends to infinity.

Includes members:
  Member "low density lattice codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural fuzzy extractors a secure way to use artificial neural networks for biometric user authentication")
  Member "gallager s exponent for mimo channels a reliability rate tradeoff":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "saddlepoint approximations for short packet wireless communications")

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)