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: "on the computation of the higher order statistics of the channel capacity for amplify and forward multihop transmission"
  Abstract: Higher-order statistics (HOS) of the channel capacity provide useful information regarding the level of reliability of the signal transmission at a particular rate. We propose in this letter a novel and unified analysis, which is based on the moment-generating function (MGF) approach, to efficiently and accurately compute the HOS of the channel capacity for amplify-and-forward multihop transmission over generalized fading channels. More precisely, our mathematical formulism is easy-to-use and tractable specifically requiring only the reciprocal MGFs of the instantaneous signal-to-noise ratio distributions of the transmission hops. Numerical and simulation results, performed to exemplify the usefulness of the proposed MGF-based analysis, are shown to be in perfect agreement.

Connected through hyperedge HE-1 (induced by source paper "on the asymptotic analysis of the high order statistics of the channel capacity over generalized fading channels"):
  with members:
    - "a novel ergodic capacity analysis of diversity combining and multihop transmission systems over generalized composite fading channels"
    - "on the computation of the higher order statistics of the channel capacity over generalized fading channels"
    - "a novel unified expression for the capacity and bit error probability of wireless communication systems over generalized fading channels"
    - "a unified mgf based capacity analysis of diversity combiners over generalized fading channels"
    - "extended generalized k egk a new simple and general model for composite fading channels"

Connected through hyperedge HE-2 (induced by source paper "on high order capacity statistics of spectrum aggregation systems over kappa mu and kappa mu shadowed fading channels"):
  with members:
    - "on the sum of correlated squared kappa mu shadowed random variables and its application to performance analysis of mrc"
    - "statistical characterization of kappa mu shadowed fading"
    - "on the computation of the higher order statistics of the channel capacity over generalized fading channels"

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)