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: "collaborative spectrum sensing from sparse observations in cognitive radio networks"
  Abstract: Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmission requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.

Connected through hyperedge HE-1 (induced by source paper "20 years of evolution from cognitive to intelligent communications"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep learning in physical layer communications"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "deep reinforcement learning based modulation and coding scheme selection in cognitive heterogeneous networks"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-2 (induced by source paper "fusion based cooperative support identification for compressive networked sensing"):
  with members:
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "amplitude aided 1 bit compressive sensing over noisy wireless sensor networks"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "recovering compressively sampled signals using partial support information"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "robust group lasso over decentralized networks"):
  with members:
    - "recoverability of group sparse signals from corrupted measurements via robust group lasso"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-5 (induced by source paper "throughput analysis of wireless powered cognitive radio networks with compressive sensing and matrix completion"):
  with members:
    - "wireless energy harvesting in a cognitive relay network"
    - "cooperative non orthogonal multiple access with simultaneous wireless information and power transfer"
    - "spatial throughput maximization of wireless powered communication networks"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"
    - "power allocation strategies in energy harvesting wireless cooperative networks"
    - "enabling wireless power transfer in cellular networks architecture modeling and deployment"
    - "mimo broadcasting for simultaneous wireless information and power transfer"
    - "matrix completion with noise"

Connected through hyperedge HE-6 (induced by source paper "sparse spectrum sensing in infrastructure less cognitive radio networks via binary consensus algorithms"):
  with members:
    - "censoring for improved sensing performance in infrastructure less cognitive radio networks"
    - "iterative byzantine vector consensus in incomplete graphs"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-7 (induced by source paper "distributed compressed sensing off the grid"):
  with members:
    - "super resolution line spectrum estimation with block priors"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "off the grid spectral compressed sensing with prior information"
    - "spectral compressive sensing with polar interpolation"
    - "spectral compressed sensing via structured matrix completion"
    - "joint sparsity recovery for spectral compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "recoverability of group sparse signals from corrupted measurements via robust group lasso"):
  with members:
    - "ripless compressed sensing from anisotropic measurements"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "compressed sensing of simultaneous low rank and joint sparse matrices"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovering low rank matrices from few coefficients in any basis"
    - "low rank matrix recovery from errors and erasures"
    - "a probabilistic and ripless theory of compressed sensing"
    - "block sparse signals uncertainty relations and efficient recovery"

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)