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: "enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access"
  Abstract: Wideband spectrum sensing detects the unused spectrum holes for dynamic spectrum access (DSA). Too high sampling rate is the main problem. Compressive sensing (CS) can reconstruct sparse signal with much fewer randomized samples than Nyquist sampling with high probability. Since survey shows that the monitored signal is sparse in frequency domain, CS can deal with the sampling burden. Random samples can be obtained by the analog-to-information converter. Signal recovery can be formulated as an L0 norm minimization and a linear measurement fitting constraint. In DSA, the static spectrum allocation of primary radios means the bounds between different types of primary radios are known in advance. To incorporate this a priori information, we divide the whole spectrum into subsections according to the spectrum allocation policy. In the new optimization model, the minimization of the L2 norm of each subsection is used to encourage the cluster distribution locally, while the L0 norm of the L2 norms is minimized to give sparse distribution globally. Because the L0/L2 optimization is not convex, an iteratively re-weighted L1/L2 optimization is proposed to approximate it. Simulations demonstrate the proposed method outperforms others in accuracy, denoising ability, etc.

Connected through hyperedge HE-1 (induced by source paper "sparse representation for wireless communications"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "common codebook millimeter wave beam design designing beams for both sounding and communication with uniform planar arrays"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "compressive sensing based multiuser detector for the large scale sm mimo uplink"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "compressed channel feedback for correlated massive mimo systems"
    - "compressive sensing based differential channel feedback for massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"

Connected through hyperedge HE-2 (induced by source paper "ultra low complexity detection of spectrum holes in compressed wideband spectrum sensing"):
  with members:
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "segmented compressed sampling for analog to information conversion method and performance analysis"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "robust compressed sensing under matrix uncertainties"):
  with members:
    - "augmented ell_1 and nuclear norm models with a globally linearly convergent algorithm"
    - "multi structural signal recovery for biomedical compressive sensing"
    - "multi sparse signal recovery for compressive sensing"
    - "the performance of orthogonal multi matching pursuit under rip"
    - "anti sampling distortion compressive wideband spectrum sensing for cognitive radio"

Connected through hyperedge HE-4 (induced by source paper "robust sparse signal recovery for compressed sensing with sampling and dictionary uncertainties"):
  with members:
    - "augmented ell_1 and nuclear norm models with a globally linearly convergent algorithm"
    - "multi structural signal recovery for biomedical compressive sensing"
    - "multi sparse signal recovery for compressive sensing"
    - "anti sampling distortion compressive wideband spectrum sensing for cognitive radio"

Connected through hyperedge HE-5 (induced by source paper "multi rate sub nyquist spectrum sensing in cognitive radios"):
  with members:
    - "aggregate interference modeling in cognitive radio networks with power and contention control"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "optimal multiband joint detection for 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)