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: "anti sampling distortion compressive wideband spectrum sensing for cognitive radio"
  Abstract: Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio in mobile communication. Compressed sensing (CS) is introduced to transfer the sampling burden. The standard sparse signal recovery of CS does not consider the distortion in the analogue-to-information converter (AIC). To mitigate performance degeneration casued by the mismatch in least square distortionless constraint which doesn't consider the AIC distortion, we define the sparse signal with the sampling distortion as a bounded additive noise, and An anti-sampling-distortion constraint (ASDC) is deduced. Then we combine the \ell1 norm based sparse constraint with the ASDC to get a novel robust sparse signal recovery operator with sampling distortion. Numerical simulations demonstrate that the proposed method outperforms standard sparse wideband spectrum sensing in accuracy, denoising ability, etc.

Connected through hyperedge HE-1 (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"
    - "enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access"

Connected through hyperedge HE-2 (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"
    - "enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access"

Connected through hyperedge HE-3 (induced by source paper "enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access"):
  with members:
    - "compressive wideband spectrum sensing for fixed frequency spectrum allocation"
    - "power efficient ultra wideband waveform design considering radio channel effects"

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)