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: "compressive link acquisition in multiuser communications"
  Abstract: An important sensing operation is to detect the presence of specific signals with unknown transmission parameters. This task, referred to as “link acquisition,” is typically a sequential search over the transmitted signal space. Recently, the use of sparsity in similar estimation or detection problems has received considerable attention. These works typically focus on the benefits of compressed sensing, but not generally on the cost brought by sparse recovery. Our goal is to examine the tradeoff in complexity and performance when using sparse recovery with compressed or uncompressed samples. To do so, we propose a compressive sparsity aware (CSA) acquisition scheme, where a compressive multichannel sampling (CMS) front-end is followed by a sparsity regularized likelihood ratio test (SR-LRT) module. The CSA scheme borrows insights from the models studied in sub-Nyquist sampling and finite rate of innovation (FRI) signals. We further optimize the CMS front-end by maximizing the average Kullback-Leibler distance of all the hypotheses in the SR-LRT. We compare the CSA scheme vis-a-vis other popular alternatives in terms of performance and complexity. Simulations suggest that one can use the CSA scheme to scale down the implementation cost with greater flexibility than other alternatives. However, we find that they both have overall complexities that scale linearly with the search space. Furthermore, it is shown that compressive measurements used in the SR-LRT lead to a performance loss when noise prevails, while providing better performance in spite of the compression when noise is mild.

Connected through hyperedge HE-1 (induced by source paper "the cognitive compressive sensing problem"):
  with members:
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "sub nyquist sampling of short pulses"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"
    - "algorithms for dynamic spectrum access with learning for cognitive radio"

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)