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: "performance of multi antenna linear mmse receivers in non homogeneous poisson and poisson cluster networks"
  Abstract: A technique is presented to evaluate the performance of a wireless link with a multi-antenna linear Minimum-Mean-Square Error (MMSE) receiver in the presence of interferers distributed according to non-homogeneous Poisson processes or Poisson cluster processes on the plane. The Cumulative Distribution Function (CDF) of the Signal-to-Interference-plus-Noise Ratio (SINR) of a representative link is derived for both types of networks assuming independent Rayleigh fading between antennas. Several representative spatial node distributions are considered, for which the derived CDFs are verified by numerical simulations. In addition, for non-homogeneous Poisson networks, it is shown that the Signal-to-Interference Ratio (SIR) converges to a deterministic non-zero value if the number of antennas at the representative receiver increases linearly with the nominal interferer density. This indicates that to the extent that the system assumptions hold, it is possible to scale such networks by increasing the number of receiver antennas linearly with user density. The results presented here are useful in characterizing the performance of multiantenna wireless networks with non-homogenous spatial node distributions and networks with clusters of users which often arise in practice, but for which few results are available.

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)