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 bayesian network model of the bit error rate for cognitive radio networks"
  Abstract: In addition to serve as platforms for dynamic spectrum access, cognitive radios can also serve as a method for improving the performance of wireless communication systems by smartly adjusting their operating parameters according to the environment and requirements. The uncertainty always present in the environment makes the practical implementation of the latter application difficult. In this paper, we propose a probabilistic graphical model, Bayesian network that captures the causal relationships among the variables bit energy to noise spectral density ratio (EbN0), carrier to interference ratio (C/I), modulation scheme (MOD), Doppler phase shift (Dop_Phi), and bit error rate (BER). BER indicates how the communication link is performing. The goal of our proposed Bayesian network is to use the BER as evidence in order to infer the behavior of the other variables, so the cognitive radio can learn how the conditions of the environment are, and based on that knowledge make better informed decisions. This model along with the method used to build it are described in this paper.

Connected through hyperedge HE-1 (induced by source paper "compressive spectrum sensing for cognitive radio networks"):
  with members:
    - "techniques for dealing with uncertainty in cognitive radio networks"
    - "double detector for sparse signal detection from one bit compressed sensing measurements"
    - "one bit compressive sensing of dictionary sparse signals"
    - "a survey of sparse representation algorithms and applications"
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "matched filter detection with dynamic threshold for cognitive radio networks"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"
    - "compressive signal processing with circulant sensing matrices"

Connected through hyperedge HE-2 (induced by source paper "techniques for dealing with uncertainty in cognitive radio networks"):
  with members:
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "matched filter detection with dynamic threshold for cognitive radio networks"
    - "wideband spectrum sensing for cognitive radio networks a survey"

Connected through hyperedge HE-3 (induced by source paper "matched filter detection with dynamic threshold for cognitive radio networks"):
  with members:
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "wideband spectrum sensing in cognitive radio 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.IT(Information Theory)