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: "packet error rate analysis of uncoded schemes in block fading channels using extreme value theory"
  Abstract: We present a generic approximation of the packet error rate (PER) function of uncoded schemes in the additive white Gaussian noise channel using extreme value theory (EVT). The PER function can assume both the exponential and the Gaussian    $Q$   -function bit error rate forms. The EVT approach leads us to a best closed-form approximation, in terms of accuracy and computational efficiency, of the average PER in block-fading channels. The numerical analysis shows that the approximation holds tight for any value of signal-to-noise ratio (SNR) and packet length whereas the earlier studies approximate the average PER only at asymptotic SNRs and packet lengths.

Connected through hyperedge HE-1 (induced by source paper "energy reliability aware link optimization for battery powered iot devices with non ideal power amplifiers"):
  with members:
    - "energy efficiency maximization of full duplex two way relay with non ideal power amplifiers and non negligible circuit power"
    - "cross layer optimization of wireless links under reliability and energy constraints"

Connected through hyperedge HE-2 (induced by source paper "renewal theoretic packet collision modeling under long tailed heterogeneous traffic"):
  with members:
    - "mobile communication systems in the presence of fading shadowing noise and interference"
    - "opportunistic wireless energy harvesting in cognitive radio networks"

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)