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: "wideband spectrum sensing in cognitive radio networks"
  Abstract: Spectrum sensing is an essential enabling functionality for cognitive radio networks to detect spectrum holes and opportunistically use the under-utilized frequency bands without causing harmful interference to legacy networks. This paper introduces a novel wideband spectrum sensing technique, called multiband joint detection, which jointly detects the signal energy levels over multiple frequency bands rather than consider one band at a time. The proposed strategy is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. The spectrum sensing problem is formulated as a class of optimization problems in interference limited cognitive radio networks. By exploiting the hidden convexity in the seemingly non-convex problem formulations, optimal solutions for multiband joint detection are obtained under practical conditions. Simulation results show that the proposed spectrum sensing schemes can considerably improve the system performance. This paper establishes important principles for the design of wideband spectrum sensing algorithms in cognitive radio networks.

Connected through hyperedge HE-1 (induced by source paper "compressive spectrum sensing for cognitive radio networks"):
  with members:
    - "techniques for dealing with uncertainty in cognitive radio networks"
    - "double detector for sparse signal detection from one bit compressed sensing measurements"
    - "one bit compressive sensing of dictionary sparse signals"
    - "a survey of sparse representation algorithms and applications"
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "matched filter detection with dynamic threshold for cognitive radio networks"
    - "a bayesian network model of the bit error rate for cognitive radio networks"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"

Connected through hyperedge HE-2 (induced by source paper "decentralized joint sparse signal recovery a sparse bayesian learning approach"):
  with members:
    - "decentralized subspace pursuit for joint sparsity pattern recovery"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "estimation of sparse mimo channels with common support"
    - "a fast distributed proximal gradient method"
    - "fast distributed gradient methods"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-3 (induced by source paper "achieving autonomous compressive spectrum sensing for cognitive radios"):
  with members:
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"

Connected through hyperedge HE-4 (induced by source paper "matched filter detection with dynamic threshold for cognitive radio networks"):
  with members:
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "a bayesian network model of the bit error rate for cognitive radio networks"
    - "wideband spectrum sensing for cognitive radio networks a survey"

Connected through hyperedge HE-5 (induced by source paper "wideband spectrum sensing with sub nyquist sampling in cognitive radios"):
  with members:
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"

Connected through hyperedge HE-6 (induced by source paper "ergodic capacity analysis in cognitive radio systems under channel uncertainty"):
  with members:
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "pilot symbol assisted communications with noncausal and causal wiener filters"
    - "training optimization for gauss markov rayleigh fading channels"

Connected through hyperedge HE-7 (induced by source paper "cognitive radio transmission under qos constraints and interference limitations"):
  with members:
    - "performance analysis of cognitive radio systems under qos constraints and channel uncertainty"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"

Connected through hyperedge HE-8 (induced by source paper "effective capacity analysis of cognitive radio channels for quality of service provisioning"):
  with members:
    - "energy efficiency in the low snr regime under queueing constraints and channel uncertainty"
    - "analysis of energy efficiency in fading channels under qos constraints"
    - "cognitive radio an information theoretic perspective"

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)