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: "modified orthogonal matching pursuit algorithm for cognitive radio wideband spectrum sensing"
  Abstract: Sampling rate is the bottleneck for spectrum sensing over multi-GHz bandwidth. Recent progress in compressed sensing (CS) initialized several sub-Nyquist rate approaches to overcome the problem. However, efforts to design CS reconstruction algorithms for wideband spectrum sensing are very limited. It is possible to further reduce the sampling rate requirement and improve reconstruction performance via algorithms considering prior knowledge of cognitive radio spectrum usages. In this paper, we group the usages of cognitive radio spectrum into three categories and propose a modified orthogonal matching pursuit (OMP) algorithm for wideband spectrum sensing. Simulation results show that this modified OMP algorithm outperforms two modified basis pursuit de-noising (BPDN) algorithms in terms of reconstruction performance and computation time.

Connected through hyperedge HE-1 (induced by source paper "cooperative wideband spectrum sensing for the centralized cognitive radio network"):
  with members:
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "compressive wideband spectrum sensing for fixed frequency spectrum allocation"

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)