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: "achieving autonomous compressive spectrum sensing for cognitive radios"
  Abstract: Compressive sensing (CS) technologies present many advantages over other existing approaches for implementing wideband spectrum sensing in cognitive radios (CRs), such as reduced sampling rate and computational complexity. However, there are two significant challenges: 1) choosing an appropriate number of sub-Nyquist measurements and 2) deciding when to terminate the greedy recovery algorithm that reconstructs wideband spectrum. In this paper, an autonomous compressive spectrum sensing (ACSS) framework is presented that enables a CR to automatically choose the number of measurements while guaranteeing the wideband spectrum recovery with a small predictable recovery error. This is realized by the proposed measurement infrastructure and the validation technique. The proposed ACSS can find a good spectral estimate with high confidence by using only a small testing subset in both noiseless and noisy environments. Furthermore, a sparsity-aware spectral recovery (SASR) algorithm is proposed to recover the wideband spectrum without requiring knowledge of the instantaneous spectral sparsity level. Such an algorithm bridges the gap between CS theory and practical spectrum sensing. Simulation results show that ACSS not only can recover the spectrum using an appropriate number of measurements but can considerably improve the spectral recovery performance as well, compared with existing CS approaches. The proposed recovery algorithm can autonomously adopt a proper number of iterations, therefore solving the problems of underfitting or overfitting, which commonly exist in most greedy recovery algorithms.

Connected through hyperedge HE-1 (induced by source paper "efficient spectrum availability information recovery for wideband dsa networks a weighted compressive sampling approach"):
  with members:
    - "weighted ell_1 minimization for sparse recovery under arbitrary prior information"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "estimating unknown sparsity in compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "recovering compressively sampled signals using partial support information"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-2 (induced by source paper "green cognitive relaying opportunistically switching between data transmission and energy harvesting"):
  with members:
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "wireless information and power transfer in relay systems with multiple antennas and interference"
    - "error rate analysis of cognitive radio transmissions with imperfect channel sensing"
    - "opportunistic wireless energy harvesting in cognitive radio networks"
    - "green power control in cognitive wireless networks"
    - "cognitive energy harvesting and transmission from a network perspective"
    - "energy efficient delay constrained transmission and sensing for cognitive radio systems"
    - "performance analysis of mimo mrc in double correlated rayleigh environments"

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)