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: "improved subspace estimation for multivariate observations of high dimension the deterministic signals case"
  Abstract: We consider the problem of subspace estimation in situations where the number of available snapshots and the observation dimension are comparable in magnitude. In this context, traditional subspace methods tend to fail because the eigenvectors of the sample correlation matrix are heavily biased with respect to the true ones. It has recently been suggested that this situation (where the sample size is small compared to the observation dimension) can be very accurately modeled by considering the asymptotic regime where the observation dimension M and the number of snapshots N converge to +∞ at the same rate. Using large random matrix theory results, it can be shown that traditional subspace estimates are not consistent in this asymptotic regime. Furthermore, new consistent subspace estimate can be proposed, which outperform the standard subspace methods for realistic values of M and N . The work carried out so far in this area has always been based on the assumption that the observations are random, independent and identically distributed in the time domain. The goal of this paper is to propose new consistent subspace estimators for the case where the source signals are modelled as unknown deterministic signals. In practice, this allows to use the proposed approach regardless of the statistical properties of the source signals. In order to construct the proposed estimators, new technical results concerning the almost sure location of the eigenvalues of sample covariance matrices of Information plus Noise complex Gaussian models are established. These results are believed to be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "report on two step knowledge aided iterative esprit algorithm"):
  with members:
    - "robust adaptive beamforming using a low complexity shrinkage based mismatch estimation algorithm"
    - "direction finding algorithms based on joint iterative subspace optimization"

Connected through hyperedge HE-2 (induced by source paper "large system analysis of a glrt for detection with large sensor arrays in temporally white noise"):
  with members:
    - "eigen inference for energy estimation of multiple sources"

Connected through hyperedge HE-3 (induced by source paper "on the smallest eigenvalue of general correlated gaussian matrices"):
  with members:
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "a new approach for capacity analysis of large dimensional multi antenna channels"

Connected through hyperedge HE-4 (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"
    - "cooperative spectrum sensing using random matrix theory"

Connected through hyperedge HE-5 (induced by source paper "asymptotic analysis of a glrt test for detection with large sensor arrays"):
  with members:
    - "eigen inference for energy estimation of multiple sources"

Connected through hyperedge HE-6 (induced by source paper "joint spatial division and multiplexing"):
  with members:
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "completely stale transmitter channel state information is still very useful"
    - "aiming perfectly in the dark blind interference alignment through staggered antenna switching"
    - "training and feedback optimization for multiuser mimo downlink"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

Connected through hyperedge HE-7 (induced by source paper "eigenvalue estimation of parameterized covariance matrices of large dimensional data"):
  with members:
    - "eigen inference for energy estimation of multiple sources"

Connected through hyperedge HE-8 (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"
    - "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"
    - "cooperative spectrum sensing using random matrix theory"
    - "a bayesian framework for collaborative multi source signal detection"

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)