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: "stronger wireless signals appear more poisson"
  Abstract: Keeler  et al.  recently derived approximation and convergence results, which imply that the point process formed from the signal strengths received by an observer in a wireless network under a general statistical propagation model can be modeled by an inhomogeneous Poisson point process on the positive real line. The basic requirement for the results to apply is that there must be a large number of transmitters with different locations and random propagation effects. The aim of this letter is to apply some of the main results in a less general but more easily applicable form to illustrate how the results can be applied in practice. New results are derived that show, it is the strongest signals, after being weakened by random propagation effects, that behave like a Poisson process, which supports recent experimental work.

Connected through hyperedge HE-1 (induced by source paper "optimizing spatial throughput in device to device networks"):
  with members:
    - "wireless network signals with moderately correlated shadowing still appear poisson"
    - "performance laws of large heterogeneous cellular networks"
    - "spectrum sharing for device to device communication in cellular networks"
    - "the performance of successive interference cancellation in random wireless networks"
    - "modeling and analysis of k tier downlink heterogeneous cellular networks"
    - "a tractable approach to coverage and rate in cellular networks"
    - "downlink performance analysis for a generalized shotgun cellular system"

Connected through hyperedge HE-2 (induced by source paper "wireless network signals with moderately correlated shadowing still appear poisson"):
  with members:
    - "ergodic spectral efficiency in mimo cellular networks"
    - "the intensity matching approach a tractable stochastic geometry approximation to system level analysis of cellular networks"
    - "studying the sinr process of the typical user in poisson networks by using its factorial moment measures"
    - "wireless networks appear poissonian due to strong shadowing"
    - "average rate of downlink heterogeneous cellular networks over generalized fading channels a stochastic geometry approach"
    - "a tractable approach to coverage and rate in cellular 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.NI(Networking and Internet Architecture)