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 multiantenna linear mmse receivers in doubly stochastic networks"
  Abstract: A technique is presented to characterize the signal-to-interference-plus-noise ratio (SINR) of a representative link with a multiantenna linear minimum-mean-square-error receiver in a wireless network with transmitting nodes distributed according to a doubly stochastic process, which is a generalization of the Poisson point process. The cumulative distribution function of the SINR of the representative link is derived, assuming independent Rayleigh fading between antennas. Several representative spatial node distributions are considered, including networks with both deterministic and random clusters, strip networks (used to model roadways, for example), hardcore networks and networks with generalized path-loss models. In addition, it is shown that if the number of antennas at the representative receiver is linearly increased with the nominal node density, the signal-to-interference ratio converges in distribution to a random variable that is nonzero in general and a positive constant in certain cases. This result 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 the node density. The results presented here are useful in characterizing the performance of multiantenna wireless networks in more general network models than what are currently available.

Connected through hyperedge HE-1 (induced by source paper "3gpp inspired hetnet model using poisson cluster process sum product functionals and downlink coverage"):
  with members:
    - "poisson cluster process bridging the gap between ppp and 3gpp hetnet models"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "enriched k tier hetnet model to enable the analysis of user centric small cell deployments"
    - "interference functionals in poisson networks"
    - "poisson hole process theory and applications to wireless networks"
    - "modeling and analyzing the coexistence of wi fi and lte in unlicensed spectrum"
    - "modeling and performance analysis of clustered device to device networks"
    - "statistical modeling and probabilistic analysis of cellular networks with determinantal point processes"

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)