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: "spectrum sensing algorithms for cognitive radio based on statistical covariances"
  Abstract: Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem in cognitive radio. Since the statistical covariances of received signal and noise are usually different, they can be used to differentiate the case where the primary user's signal is present from the case where there is only noise. In this paper, spectrum sensing algorithms are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. Two test statistics are then extracted from the sample covariance matrix. A decision on the signal presence is made by comparing the two test statistics. Theoretical analysis for the proposed algorithms is given. Detection probability and associated threshold are found based on statistical theory. The methods do not need any information of the signal, the channel and noise power a priori. Also, no synchronization is needed. Simulations based on narrowband signals, captured digital television (DTV) signals and multiple antenna signals are presented to verify the methods.

Connected through hyperedge HE-1 (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"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-2 (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"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-3 (induced by source paper "another look in the analysis of cooperative spectrum sensing over nakagami m fading channels"):
  with members:
    - "analytic expressions and bounds for special functions and applications in communication theory"

Connected through hyperedge HE-4 (induced by source paper "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"):
  with members:
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "compressive and noncompressive power spectral density estimation from periodic nonuniform samples"
    - "noise folding in compressed sensing"
    - "wideband spectrum sensing at sub nyquist rates"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-5 (induced by source paper "jamming games in the mimo wiretap channel with an active eavesdropper"):
  with members:
    - "a secure communication game with a relay helping the eavesdropper"
    - "robust beamforming for security in mimo wiretap channels with imperfect csi"
    - "secure transmission with artificial noise over fading channels achievable rate and optimal power allocation"
    - "half duplex active eavesdropping in fast fading channels a block markov wyner secrecy encoding scheme"
    - "the secrecy capacity of the mimo wiretap channel"
    - "mutual information games in multi user channels with correlated jamming"

Connected through hyperedge HE-6 (induced by source paper "eigenvalue based sensing and snr estimation for cognitive radio in presence of noise correlation"):
  with members:
    - "mimo interference alignment over correlated channels with imperfect csi"
    - "cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in wishart matrices"
    - "cooperative spectrum sensing using random matrix theory"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-7 (induced by source paper "prescient precoding in heterogeneous dsa networks with both underlay and interweave mimo cognitive radios"):
  with members:
    - "beamforming and rate allocation in miso cognitive radio networks"
    - "cognitive radio with partial channel state information at the transmitter"
    - "dirty paper coding for the mimo cognitive radio channel with imperfect csit"
    - "cooperative multi cell block diagonalization with per base station power constraints"
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"
    - "on the fading paper achievable region of the fading mimo broadcast channel"
    - "cognitive radio an information theoretic perspective"

Connected through hyperedge HE-8 (induced by source paper "a sub space method to detect multiple wireless microphone signals in tv band white space"):
  with members:
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

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)