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: "eigenvalue based spectrum sensing algorithms for cognitive radio"
  Abstract: Spectrum sensing is a fundamental component is a cognitive radio. In this paper, we propose new sensing methods based on the eigenvalues of the covariance matrix of signals received at the secondary users. In particular, two sensing algorithms are suggested, one is based on the ratio of the maximum eigenvalue to minimum eigenvalue; the other is based on the ratio of the average eigenvalue to minimum eigenvalue. Using some latest random matrix theories (RMT), we quantify the distributions of these ratios and derive the probabilities of false alarm and probabilities of detection for the proposed algorithms. We also find the thresholds of the methods for a given probability of false alarm. The proposed methods overcome the noise uncertainty problem, and can even perform better than the ideal energy detection when the signals to be detected are highly correlated. The methods can be used for various signal detection applications without requiring the knowledge of signal, channel and noise power. Simulations based on randomly generated signals, wireless microphone signals and captured ATSC DTV signals are presented to verify the effectiveness of the proposed methods.

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 "intelligent wireless communications enabled by cognitive radio and machine learning"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations"
    - "deep learning for wireless physical layer opportunities and challenges"

Connected through hyperedge HE-3 (induced by source paper "utility maximizing sequential sensing over a finite horizon"):
  with members:
    - "reliability of sequential hypothesis testing can be achieved by an almost fixed length test"
    - "opportunistic advertisement scheduling in live social media a multiple stopping time pomdp approach"
    - "optimal index policies for anomaly localization in resource constrained cyber systems"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "censored truncated sequential spectrum sensing for cognitive radio networks"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"

Connected through hyperedge HE-4 (induced by source paper "time limited toeplitz operators on abelian groups applications in information theory and subspace approximation"):
  with members:
    - "the eigenvalue distribution of discrete periodic time frequency limiting operators"
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "fast algorithms for the computation of fourier extensions of arbitrary length"
    - "greed is super a fast algorithm for super resolution"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"
    - "on landau s eigenvalue theorem and information cut sets"
    - "compressive sensing of analog signals using discrete prolate spheroidal sequences"

Connected through hyperedge HE-5 (induced by source paper "unified analysis of cooperative spectrum sensing over composite and generalized fading channels"):
  with members:
    - "analytic solutions to a marcum q function based integral and application in energy detection of unknown signals over multipath fading channels"
    - "statistical characterization of kappa mu shadowed fading"
    - "spectrum sensing algorithms for cognitive radio based on statistical covariances"

Connected through hyperedge HE-6 (induced by source paper "on the asymptotic equivalence of circulant and toeplitz matrices"):
  with members:
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"

Connected through hyperedge HE-7 (induced by source paper "performance analysis of energy detection over mixture gamma based fading channels with diversity reception"):
  with members:
    - "analytic solutions to a marcum q function based integral and application in energy detection of unknown signals over multipath fading channels"
    - "spectrum sensing algorithms for cognitive radio based on statistical covariances"

Connected through hyperedge HE-8 (induced by source paper "hard decision cooperative spectrum sensing based on estimating the noise uncertainty factor"):
  with members:
    - "soft decision cooperative spectrum sensing based upon noise uncertainty estimation"
    - "eigenvalue based sensing and snr estimation for cognitive radio in presence of noise correlation"

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)