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: "a novel ergodic capacity analysis of diversity combining and multihop transmission systems over generalized composite fading channels"
  Abstract: Ergodic capacity is an important performance measure associated with reliable communication at the highest rate at which information can be sent over the channel with a negligible probability of error. In the shadow of this definition, diversity receivers (such as selection combining, equal-gain combining and maximal-ratio combining) and transmission techniques (such as cascaded fading channels, amplify-and-forward multihop transmission) are deployed in mitigating various performance impairing effects such as fading and shadowing in digital radio communication links. However, the exact analysis of ergodic capacity is in general not always possible for all of these forms of diversity receivers and transmission techniques over generalized composite fading environments due to it's mathematical intractability. In the literature, published papers concerning the exact analysis of ergodic capacity have been therefore scarce (i.e., only [1] and [2]) when compared to those concerning the exact analysis of average symbol error probability. In addition, they are essentially targeting to the ergodic capacity of the maximal ratio combining diversity receivers and are not readily applicable to the capacity analysis of the other diversity combiners / transmission techniques. In this paper, we propose a novel moment generating function-based approach for the exact ergodic capacity analysis of both diversity receivers and transmission techniques over generalized composite fading environments. As such, we demonstrate how to simultaneously treat the ergodic capacity analysis of all forms of both diversity receivers and multihop transmission techniques.

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:
    - "on the computation of the higher order statistics of the channel capacity for amplify and forward multihop transmission"
    - "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 the relationships between average channel capacity average bit error rate outage probability and outage capacity over additive white gaussian noise channels"):
  with members:
    - "on the asymptotic analysis of the high order statistics of the channel capacity over generalized fading channels"
    - "asymptotic capacity analysis for adaptive transmission schemes under general fading distributions"
    - "on the computation of the higher order statistics of the channel capacity over generalized fading channels"
    - "a unified mgf based capacity analysis of diversity combiners over generalized fading channels"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "exact performance analysis of partial relay selection based on shadowing side information over generalized composite fading channels"):
  with members:
    - "a novel unified expression for the capacity and bit error probability of wireless communication systems over generalized fading channels"
    - "a simple cooperative diversity method based on network path selection"

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)