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 model for randomized resource allocation in decentralized wireless networks"
  Abstract: In this paper, we consider a decentralized wireless communication network with a fixed number $u$ of frequency sub-bands to be shared among $N$ transmitter-receiver pairs. It is assumed that the number of active users is a random variable with a given probability mass function. Moreover, users are unaware of each other's codebooks and hence, no multiuser detection is possible. We propose a randomized Frequency Hopping (FH) scheme in which each transmitter randomly hops over a subset of $u$ sub-bands from transmission to transmission. We derive lower and upper bounds on the mutual information of each user and demonstrate that, for large Signal-to-Noise Ratio (SNR) values, the two bounds coincide. This observation enables us to compute the sum multiplexing gain of the system and obtain the optimum hopping strategy for maximizing this quantity. We compare the performance of the FH system with that of the Frequency Division (FD) system in terms of several performance measures and show that (depending on the probability mass function of the number of active users) the FH system can offer a significant improvement implying a more efficient usage of the spectrum.

Connected through hyperedge HE-1 (induced by source paper "wireless networks with asynchronous users"):
  with members:
    - "randomized vs orthogonal spectrum allocation in decentralized networks outage analysis"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "gaussian interference channel capacity to within one bit"
    - "cognitive radio an information theoretic perspective"
    - "capacity limits of cognitive radio with distributed and dynamic spectral activity"

Connected through hyperedge HE-2 (induced by source paper "randomized vs orthogonal spectrum allocation in decentralized networks outage analysis"):
  with members:
    - "bandwidth partitioning in decentralized wireless networks"
    - "fractional power control for decentralized wireless networks"
    - "throughput scaling laws for wireless networks with fading channels"
    - "rate constrained wireless networks with fading channels interference limited and noise limited regimes"
    - "the effect of fading channel inversion and threshold scheduling on ad hoc networks"
    - "on the user selection for mimo broadcast channels"
    - "cognitive radio an information theoretic perspective"
    - "capacity limits of cognitive radio with distributed and dynamic spectral activity"

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)