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: "cooperative spectrum sensing using random matrix theory"
  Abstract: In this paper, using tools from asymptotic random matrix theory, a new cooperative scheme for frequency band sensing is introduced for both AWGN and fading channels. Unlike previous works in the field, the new scheme does not require the knowledge of the noise statistics or its variance and is related to the behavior of the largest and smallest eigenvalue of random matrices. Remarkably, simulations show that the asymptotic claims hold even for a small number of observations (which makes it convenient for time-varying topologies), outperforming classical energy detection techniques.

Connected through hyperedge HE-1 (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-2 (induced by source paper "the random matrix regime of maronna s m estimator for observations corrupted by elliptical noises"):
  with members:
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "improved subspace estimation for multivariate observations of high dimension the deterministic signals case"

Connected through hyperedge HE-3 (induced by source paper "statistical inference in large antenna arrays under unknown noise pattern"):
  with members:
    - "eigen inference for energy estimation of multiple sources"
    - "cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in wishart matrices"
    - "a central limit theorem for the sinr at the lmmse estimator output for large dimensional signals"

Connected through hyperedge HE-4 (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"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"
    - "spectrum sensing algorithms for cognitive radio based on statistical covariances"

Connected through hyperedge HE-5 (induced by source paper "a cooperative bayesian nonparametric framework for primary user activity monitoring in cognitive radio networks"):
  with members:
    - "cooperative games with overlapping coalitions"

Connected through hyperedge HE-6 (induced by source paper "signal processing in large systems a new paradigm"):
  with members:
    - "random beamforming over quasi static and fading channels a deterministic equivalent approach"
    - "improved subspace estimation for multivariate observations of high dimension the deterministic signals case"
    - "eigen inference for energy estimation of multiple sources"
    - "finite dimensional statistical inference"
    - "random beamforming over correlated fading channels"
    - "a deterministic equivalent for the analysis of correlated mimo multiple access channels"
    - "on the capacity achieving covariance matrix for frequency selective mimo channels using the asymptotic approach"
    - "a bayesian framework for collaborative multi source signal detection"

Connected through hyperedge HE-7 (induced by source paper "collaborative spectrum sensing from sparse observations in cognitive radio networks"):
  with members:
    - "collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks"
    - "dynamic updating for ell_ 1 minimization"
    - "matrix completion from a few entries"
    - "coalitional games for distributed collaborative spectrum sensing in cognitive radio networks"

Connected through hyperedge HE-8 (induced by source paper "coalition formation games for collaborative spectrum sensing"):
  with members:
    - "coalitional games for distributed collaborative spectrum sensing in cognitive radio networks"
    - "a generic approach to coalition formation"

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)